Pack — 1 Information Representation (AS Level)

Pack — 1 Information Representation

Question-and-answer mirror of Jigsaw. Every concept in Jigsaw appears here as a !question with a collapsible !success answer.

How marks are set

• 9618 | — the highest mark tariff that concept has ever carried in a 9618 paper. Answer points = that tariff + 2 spare, newest mark scheme first, older ones filling the gaps.
• 9608 | — same rule, using 9608 tariffs. Still inside the 9618 syllabus wording, but untested in the current series.
• Inferred | — not tested in either series. Tariff estimated from how 9618 marks comparable questions.
• SME | — from the Save My Exams notes. Tariff estimated the same way.

One bullet = one mark, unless the bullet begins … (an expansion of the point above it).

This Pack is a drill, not a reading

Topic 1 is the most procedural topic in the paper — most of it is conversions and calculations. Do not read the answers; cover them, write the working, then check. A correct answer with no working caps at 1 mark in almost every calculation question here.

The three slips that cost the most marks

1 — The unit. The divisor changes with the unit asked for: ÷1000 for kB/MB/GB, ÷1024 for KiB/MiB/GiB. Right arithmetic, wrong unit = working mark only. 2 — Bit depth given in bytes. s24_qp_13_sc_2.a gives a bit depth of “4 bytes”. There is then no ÷ 8. 3 — Justification questions. Several carry no mark for the choice — all the marks are in the reasoning.

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1.1 Data Representation

1.1.1 Binary magnitudes and prefixes

Question

State one difference between a tebibyte and a gigabyte.

Answer — 2 routes, 1 mark

• By value: a tebibyte = 1024 gibibytes // 1 048 576 kibibytes // 2⁴⁰ bytes, whereas a gigabyte = 1000 megabytes // 1 000 000 kilobytes // 10⁹ bytes
• By prefix type: tebi is a binary prefix and giga is a denary prefix
• The second route is one line and scores the same mark — use it under time pressure
• Same answer shape for kibibyte vs megabyte and kibibyte vs kilobyte

Latest: 9618_w24_qp_11_sc_1.a (also w23_qp_12_sc_3.a, s23_qp_11_sc_3.d.i)

Question

Complete the following description. A kibibyte has a ______ prefix. Three kibibytes is the same as ______ bytes. A megabyte has a ______ prefix. Two terabytes is the same as ______ gigabytes.

Answer — 1 mark for each correct answer

• A kibibyte has a binary prefix
• Three kibibytes is the same as 3072 bytes (3 × 1024)
• A megabyte has a decimal / denary prefix
• Two terabytes is the same as 2000 gigabytes (2 × 1000)
• Note the question mixes prefix-type answers with arithmetic answers in the same four marks

Latest: 9618_s24_qp_13_sc_1.a

Question

Tick one box only to identify the largest file size: 3300 kibibytes · 0.3 megabytes · 3 mebibytes · 3300 kilobytes.

Answer — 1 mark

• 3300 kibibytes = 3300 × 1024 = 3 379 200 bytes
• 3 mebibytes = 3 × 1 048 576 = 3 145 728 bytes
• 3300 kilobytes = 3 300 000 bytes
• 0.3 megabytes = 300 000 bytes
• The trap is that “mebibytes” sounds larger — convert everything to bytes before comparing

Latest: 9618_s24_qp_12_sc_7.a

Question

Draw one line from each binary value to its equivalent value: 8 bits · 8000 bits · 1000 kilobytes · 1024 mebibytes · 8192 bits.

Answer — 1 mark per correct line

• 8 bits → 1 byte
• 8000 bits → 1 kilobyte (8000 ÷ 8 = 1000 bytes)
• 1000 kilobytes → 1 megabyte
• 1024 mebibytes → 1 gibibyte
• 8192 bits → 1 kibibyte (8192 ÷ 8 = 1024 bytes)
• 1 gigabyte and 1 mebibyte are distractors with no matching value

Latest: 9618_w21_qp_11_sc_1.a

Question

Explain why computer systems use both binary prefixes and denary prefixes.

Answer — 5 points for 3 marks

• Computers address memory in powers of 2, so memory sizes are naturally multiples of 1024
• … so binary prefixes (kibi, mebi, gibi) describe memory exactly
• Denary prefixes are based on the assumption that 1 kilo = 1000, from the base-10 system humans use
• Storage manufacturers quote capacity in denary prefixes, partly because the numbers are larger
• This is why a drive sold as “1 TB” shows as roughly 0.91 TiB on a computer — the same bytes, two naming systems

Inference: every question in either series tests the difference between the two systems; none asks the reason they both exist.

Question

Complete the table giving the number of bytes represented by each of kilo/kibi, mega/mebi, giga/gibi and tera/tebi.

Answer — 1 mark per row, 4 marks

• kilobyte 10³ = 1000 · kibibyte 2¹⁰ = 1024
• megabyte 10⁶ = 1 000 000 · mebibyte 2²⁰ = 1 048 576
• gigabyte 10⁹ · gibibyte 2³⁰ = 1 073 741 824
• terabyte 10¹² · tebibyte 2⁴⁰
• The next row, not in the syllabus: petabyte 10¹⁵ · pebibyte 2⁵⁰

SME’s rule: be precise about memory (binary prefixes); a rough estimate is acceptable for storage (denary prefixes).

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1.1.2 Number systems

Question

Put one tick in each row to identify the minimum number of bits used to store each example of data.

Answer — 3 marks for 6 correct ticks, 2 for 4–5, 1 for 2–3

• The hexadecimal value F139 → 16 (4 hex digits × 4 bits)
• 16 000 000 unique amplitude values → 24 (2²⁴ = 16 777 216)
• An IPv4 address → 32
• 256 unique colours → 8 (2⁸ = 256)
• An IPv6 address → 128
• The denary value 65 000 → 16 (2¹⁶ = 65 536)
• Synoptic: needs IP addressing from Topic 2 and colour depth from 1.2

Latest: 9618_w25_qp_13_sc_2.a

Question

State the number of unique binary values that can be represented in 16 bits.

Answer — 1 mark

• 2¹⁶ // 65 536
• The general rule: n bits give 2ⁿ distinct values
• The same rule gives colour depth (2ⁿ colours) and sampling resolution (2ⁿ amplitudes)

Latest: 9618_s23_qp_12_sc_4.a

Question

Draw one line from each description to its matching denary value: the smallest integer in 8-bit two’s complement · the largest integer in 8-bit two’s complement · the largest unsigned integer in 8 bits.

Answer — 1 mark per correct line

• The smallest integer in 8-bit two’s complement → −128
• The largest integer in 8-bit two’s complement → 127
• The largest unsigned integer in 8 bits → 255
• The distractors (−127, −255, −256, 256, 128) are all near-misses
• Remember: the two’s complement range is −2ⁿ⁻¹ to +2ⁿ⁻¹ − 1 — not symmetrical

Latest: 9618_s23_qp_13_sc_7.a

Question

Write the smallest and the largest two’s complement binary integers that can be represented in 8 bits.

Answer — 1 mark each

• Smallest: 1000 0000 (= −128)
• Largest: 0111 1111 (= +127)
• Read carefully whether the binary pattern or the denary value is wanted — both versions are set

Latest: 9618_s25_qp_12_sc_2.b.ii (also s23_qp_11_sc_3.d.iv)

Question

State two benefits of using Binary Coded Decimal (BCD) to represent values.

Answer — 1 mark per benefit, max 2

• It is straightforward to convert to and from denary
• … so it is less complex to encode and decode for programmers
• It is easier for digital equipment that uses BCD to display output information
• It can represent monetary values exactly

Latest: 9618_w22_qp_13_sc_9.b

Question

State why the value in the register cannot be interpreted as Binary Coded Decimal (BCD).

Answer — 1 mark

• The denary value in each group of 4 bits is greater than 9
• … // the denary value in each nibble is greater than 9
• Any nibble from 1010 to 1111 is invalid BCD, because BCD only uses 0000–1001

Latest: 9618_w21_qp_12_sc_4.d

Question

Give the 8-bit one’s complement representation of the denary number −120. Show your working.

Answer — 1 mark for working, 1 for the answer

• Working: +120 = 0111 1000
• Answer: 1000 0111
• One’s complement is just invert every bit — no “add 1”
• This is the only one’s complement question in the 9618 series

Latest: 9618_s23_qp_12_sc_4.b

Question

Explain one disadvantage of using one’s complement rather than two’s complement to represent negative numbers.

Answer — 4 points for 2 marks

• One’s complement has two representations for zero
• … 0000 0000 (positive zero) and 1111 1111 (negative zero)
• … which wastes a value and complicates comparison and arithmetic
• Two’s complement has only one representation of zero, and the range is one value larger

Inference: the syllabus names one’s and two’s complement. 9618 has asked one’s complement exactly once, as a conversion. Its defining weakness has never been examined in either series, though SME teaches it.

Question

Explain why computers use two’s complement rather than sign-and-magnitude to represent negative numbers.

Answer — 5 points for 3 marks

• Subtraction can be carried out as addition of the negative
• … so the same adder circuit handles both operations, and no separate subtraction hardware is needed
• The process is the same for any combination of signs — no special logic to detect the signs first
• There is only one representation of zero
• The leftmost column simply becomes negative (−128 in 8 bits), so the normal column-value method still works

Inference: untested in either series. SME makes the hardware-simplicity point explicitly; no mark scheme does.

Question

Describe what is meant by a number base, and state the base of binary, denary and hexadecimal.

Answer — 4 points for 2 marks

• A number base is the number of distinct digits or symbols the number system uses
• Each column represents a power of the base, increasing from the right
• Binary is base 2 (digits 0–1) · denary is base 10 (0–9) · hexadecimal is base 16 (0–9 then A–F)
• One hexadecimal digit represents exactly four bits — one nibble

Inference/SME: every question uses the bases; none asks what a base means. The nibble relationship is what makes binary↔hex conversion instant.

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1.1.3 Converting between bases and representations

Question

Convert the binary number 101100111010 into hexadecimal.

Answer — 1 mark

• Split into nibbles from the right: 1011 | 0011 | 1010
• 1011 = 11 = B · 0011 = 3 · 1010 = 10 = A
• Answer: B3A
• More: 110001100111 → C67 · 1110001100111011 → E33B

Latest: 9618_w25_qp_13_sc_2.d (also w25_qp_11_sc_1.a.i, w24_qp_11_sc_1.b.i)

Question

Convert the hexadecimal number C0F into denary. Show your working.

Answer — 1 mark for working, 1 for the answer

• Working: C = 12, so (12 × 16²) + (0 × 16¹) + (15 × 16⁰)
• = 3072 + 0 + 15
• Answer: 3087
• Also: 1FAB → 8107

Latest: 9618_s24_qp_12_sc_7.c (also w24_qp_13_sc_8.a)

Question

Convert the denary number 241 to hexadecimal.

Answer — 1 mark

• 241 ÷ 16 = 15 remainder 1
• First digit = 15 = F, second digit = 1
• Answer: F1
• Alternative route: 241 = 1111 0001 in binary → F1

Latest: 9618_s24_qp_13_sc_1.b

Question

Convert the denary integer 558 into 12-bit binary and hexadecimal.

Answer — 1 mark each

• Binary: 558 = 512 + 32 + 8 + 4 + 2 = 0010 0010 1110
• Hexadecimal: group the nibbles — 0010 = 2, 0010 = 2, 1110 = E → 22E
• Doing the binary first gives the hexadecimal free

Latest: 9618_s25_qp_12_sc_2.a

Question

Convert the denary number −108 into a 12-bit two’s complement binary number.

Answer — 1 mark

• +108 = 0000 0110 1100 (pad to 12 bits first)
• Invert: 1111 1001 0011
• Add 1: 1111 1001 0100
• Also: −196 → 1111 0011 1100
• Pad to the full width before inverting — inverting 8 bits then padding gives the wrong answer

Latest: 9618_w25_qp_13_sc_2.b (also w23_qp_12_sc_3.b.i)

Question

Convert the 12-bit two’s complement binary integer 111110111100 into denary. Show your working.

Answer — 1 mark for the working, 1 for the denary value

• Method 1: flip the bits → 0000 0100 0011, add 1 → 0000 0100 0100 = 68, so the value is −68
• Method 2: treat the MSB as negative — −2048 + 1024 + 512 + 256 + 128 + 32 + 16 + 8 + 4 = −68
• Both methods are credited
• More: 11100010 → −30 · 10010110 → −106 · 100110010111 → −1641 · 11001101 → −51

Latest: 9618_w25_qp_11_sc_1.a.iii (also s25_qp_12_sc_2.b.i, w24_qp_11_sc_1.b.ii)

Question

Explain how to convert the two’s complement integer 10011111 into denary. Give the denary value after conversion.

Answer — 1 mark per method bullet (max 2) + 1 for the conversion

• Flip each bit then add 1 …
• … then use the method of converting the new binary number into denary (and make it negative)
• or: the most significant 1 bit is treated as the corresponding negative denary value …
• … then add the other positive corresponding denary values
• Denary value: −97
• The command word is explain — the method must be written out, not just the answer

Latest: 9618_w24_qp_13_sc_8.b

Question

Convert the denary number 964 into Binary Coded Decimal (BCD).

Answer — 1 mark

• 9 → 1001 · 6 → 0110 · 4 → 0100
• Answer: 1001 0110 0100
• Also: 108 → 0001 0000 1000 — the leading zeros of 0001 must be written
• BCD codes each digit separately; it is not the binary value of the whole number

Latest: 9618_s23_qp_11_sc_3.d.ii (also w25_qp_11_sc_1.a.ii)

Question

Convert the Binary Coded Decimal value 100001100101 into denary.

Answer — 1 mark

• Split into nibbles: 1000 | 0110 | 0101
• 1000 = 8 · 0110 = 6 · 0101 = 5
• Answer: 865
• Also: 010101110011 → 573

Latest: 9618_w23_qp_12_sc_3.b.ii (also w24_qp_11_sc_1.b.iii)

Question

Convert the unsigned binary integer 10010101 into Binary Coded Decimal (BCD). Show your working.

Answer — 1 mark for each bullet

• Working: 10010101 = 128 + 16 + 4 + 1 = 149 decimal
• Answer: 1 → 0001, 4 → 0100, 9 → 1001 = 0001 0100 1001
• The intermediate denary value is itself a mark — always write it down

Latest: 9618_w22_qp_12_sc_2.a.iii

Question

Complete the table by filling in the missing numbers for each extended ASCII character.

Answer — 1 mark for each correctly completed space

• ! denary 33, hex 21 → 8-bit binary 0010 0001
• L binary 0100 1100, hex 4C → denary 76
• ü denary 252, binary 1111 1100 → hexadecimal FC
• Three conversions, each in a different direction — read which column is blank

Latest: 9618_s25_qp_13_sc_1.c.ii

Question

Convert the hexadecimal number 5F into binary.

Answer — 1 mark

• Each hex digit becomes one nibble
• 5 → 0101 · F = 15 → 1111
• Answer: 0101 1111
• The easiest of the nine conversions, and the only one 9618 has never set on its own

Inference: 9618 asks binary→hex repeatedly and hex→denary repeatedly, but a bare “convert this hexadecimal into binary” appears only inside character tables, never as its own question.

Question

Convert the denary number −68 into a 12-bit two’s complement value, and give that value in hexadecimal.

Answer — 1 mark for the two's complement, 1 for the hex

• +68 = 0000 0100 0100
• Invert and add 1 → 1111 1011 1100
• Group into nibbles: 1111 = F, 1011 = B, 1100 = C
• Hexadecimal: FBC

Inference: all hexadecimal conversions in 9618 use positive values, though both halves of this have been separately examined.

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1.1.4 Binary addition and subtraction

Question

Perform the following binary addition. Show your working. 10101010 + 00110111

Answer — 1 mark for the answer, 1 for the working

•   1010 1010
  + 0011 0111
  ───────────
    1110 0001
     1 1 1 1 1   ← carries
  

• Answer: 1110 0001
• Write the carry row — it is literally the working mark

Latest: 9618_w21_qp_11_sc_1.b.i

Question

Add the denary number 15 to the binary number 00100011. Perform the addition in binary and give your answer in binary. Show your working.

Answer — 1 mark per bullet

• Converting 15 to binary: 0000 1111
• Method for the addition (the carry row shown)

•   0010 0011
  + 0000 1111
  ───────────
    0011 0010
       1  111   ← carries
  

• Final answer: 0011 0010
• The conversion step is a separate mark from the addition

Latest: 9618_s21_qp_11_sc_1.c.ii

Question

Subtract the denary number 10 from the two’s complement representation 00100011. Give your answer in binary and show your working.

Answer — 1 mark per bullet

• Converting −10 to two’s complement binary: 10 = 0000 1010, so −10 = 1111 0110
• Adding the values

•   0010 0011
  + 1111 0110
  ───────────
    0001 1001
      11   11   ← carries
  

• Final answer: 0001 1001
• The carry out of the top bit is discarded — that is normal, not an overflow

Latest: 9618_s21_qp_11_sc_1.c.iii (also w24_qp_11_sc_1.c)

Question

Subtract the denary number 10 from the denary number 100 using binary subtraction. Show your working.

Answer — 1 mark each

• Converting both: 100 = 0110 0100 and 10 = 0000 1010
• Subtraction method — convert 10 to −10 and add, or direct subtraction
• Correct answer: 0101 1010
• Method 1 (two’s complement): −10 = 1111 0110; 0110 0100 + 1111 0110 = (1)0101 1010
• Method 2 (direct): borrow as in denary — 0110 0100 − 0000 1010 = 0101 1010
• Both methods are equally credited — direct borrowing is not penalised

Latest: 9618_s24_qp_12_sc_7.b

Question

State how an overflow can occur when adding two binary integers.

Answer — 2 phrasings for 1 mark

• The result is a larger number than can be stored in the given number of bits
• … // the result is greater than 255 (for 8 unsigned bits)

Latest: 9618_w21_qp_11_sc_1.b.ii

Question

Explain how overflow can occur when adding two signed binary integers, and describe its effect.

Answer — 5 points for 3 marks

• In a signed representation the leftmost bit is the sign bit
• Adding two large positive numbers can carry into the sign bit
• … which flips the sign, so the result appears negative
• … e.g. in 8 bits, 127 + 1 gives 1000 0000 = −128
• The extra bits are truncated or wrapped around, so the result is incorrect or unpredictable
• The same applies adding two large negatives, which can produce a positive

Inference: the syllabus says “show understanding of how overflow can occur”, but there is exactly one 9618 question on it — 1 mark, in w21. Sign-bit overflow is in SME and in no mark scheme.

Question

When 48 − 12 is performed in 8-bit two’s complement, a ninth bit is produced. Explain why this is ignored.

Answer — 4 points for 2 marks

• 48 = 0011 0000, −12 = 1111 0100; adding gives (1)0010 0100
• 0010 0100 = 36, which is the correct answer
• The ninth bit is a by-product of the two’s complement method, not part of the value
• True overflow is different — that is when the result itself will not fit in the available bits

SME’s worked example; the distinction between a discarded carry and a genuine overflow is in no mark scheme.

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1.1.5 Practical applications of BCD and hexadecimal

Question

Give one application where Binary Coded Decimal (BCD) is used and justify its use.

Answer — 1 mark for the application, 1 for a corresponding justification

• Financial / banking calculations … because transactions use only two decimal places and must be accurate, with no accumulating or rounding errors, and it is difficult to represent decimal values exactly in normal binary
• Electronic displays (calculators, digital clocks) … because visual displays only need to show individual digits
• … and because conversion between denary and BCD is straightforward
• Storage of the date and time in the BIOS of a PC … because conversion between denary and BCD is more straightforward
• Barcode systems … because conversion between denary and BCD can be accurately completed
• The justification must match the application named — a mismatched pair scores 1

Latest: 9618_s25_qp_12_sc_2.c (also w23_qp_12_sc_3.c)

Question

Describe a use of BCD number representation.

Answer — 3 points for 2 marks

• When denary numbers need to be electronically coded
• … e.g. to operate the displays on a calculator, where each digit is represented separately
• Decimal fractions can be accurately represented
• Also credited: any scenario where a single digit needs to be transmitted or displayed — a calculator, a digital clock

Latest: 9608_s15_qp_13_sc_1.b.ii (also w19_qp_11_sc_5.c.ii, w17_qp_13_sc_1.b.iii). The “electronically coded” framing is 9608-only.

Question

Describe two practical applications where hexadecimal is used, and justify its use in each.

Answer — 2 marks per application (use + justification), 4 marks

• MAC addresses and IPv6 addresses … a 48-bit or 128-bit address written in binary would be enormously long and error-prone; hexadecimal is far shorter and easier to read and transcribe
• Colour codes in HTML / CSS (e.g. #FF0000) … two hex digits give the 0–255 value of each of red, green and blue in a compact form
• Memory addresses and memory dumps … hexadecimal is a compact shorthand for long binary addresses, and one hex digit maps exactly to four bits, so conversion is trivial
• Assembly language operands and error codes … values can be written compactly and converted to binary at a glance
• Character code tables (ASCII and Unicode) … codes are conventionally quoted in hexadecimal
• The underlying justification in every case: hexadecimal is easier for humans to read and less error-prone than binary, while converting to and from binary instantly

Inference: the single clearest gap in Topic 1. The bullet is titled “practical applications where Binary Coded Decimal (BCD) and Hexadecimal are used”, yet every question in both series asks only about BCD. Several of these applications are already examined elsewhere (MAC and IPv6 in Topic 2, memory addresses in Topic 4), which makes a synoptic question easy to set.

Question

State two drawbacks of using Binary Coded Decimal to represent values.

Answer — 4 points for 2 marks

• BCD wastes storage space
• … because 4 bits can represent 16 patterns but BCD uses only 10 of them
• Arithmetic is more complex than in pure binary
• … so it needs more processing, or dedicated hardware, to perform calculations

Inference: both series ask only for the benefits of BCD. The costs are the obvious counterweight and have never been asked.

Question

Give four situations where BCD is used and explain why BCD suits each.

Answer — 1 mark per use with its reason, 4 marks

• Electronic calculators — keeps numbers in decimal format for easier display and accuracy
• Digital clocks and watches — time is naturally decimal (12:45), so the display logic is simpler
• Banking and financial systems — avoids rounding errors in decimal calculations, especially with money
• Older digital and embedded systems — simpler to implement with hardware that drives one digit at a time

9618’s own BCD question is marked at 2 (application + justification); four pairs justifies 4.

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1.1.6 Character sets

Question

Explain how text is represented by the ASCII character set.

Answer — 1 mark per bullet, max 2

• Each character has a unique code
• Each character in the text is replaced sequentially / in order by its code
• The codes are stored in the order they appear in the word
• The same three-part shape as the bitmap and sound answers: unit → unique code → stored in sequence

Latest: 9618_w25_qp_13_sc_1.b (also s24_qp_13_sc_1.d.ii, s21_qp_12_sc_6.b)

Question

Give one similarity and two differences between the ASCII and Unicode character sets.

Answer — 1 mark for the similarity, 2 for differences

• Similarity (max 1): both can use 8 bits
• … // both represent each character using a unique code
• … // Unicode contains all the characters ASCII contains — ASCII is a subset of Unicode
• Difference: Unicode can go up to 32 bits per character whereas ASCII is 7 or 8
• Difference: Unicode can represent a wider range of characters than ASCII
• Difference: different languages are represented in Unicode; ASCII is only for one
• A similarity written in the difference box scores nothing — the boxes are marked separately

Latest: 9618_w22_qp_11_sc_1.c

Question

Give two differences between the ASCII and Unicode character sets.

Answer — 1 mark per bullet, max 2

• ASCII uses 7 or 8 bits; Unicode can use many more — up to 32
• Unicode can represent a wider range of characters, including different languages

Latest: 9618_w25_qp_13_sc_1.c

Question

Give two advantages of using the Unicode character set instead of the ASCII character set.

Answer — 1 mark each to max 2

• A wider range of characters can be represented
• … so characters from more languages can be represented
• … and symbols such as emojis can be used

Latest: 9618_s25_qp_11_sc_3.b.i

Question

Give two characteristics of the Unicode character set.

Answer — 1 mark each to max 2

• 8 / 16 / 32 bits per character
• Represents 2⁸ / 2¹⁶ etc. characters
• Represents every language and other characters such as emojis

Latest: 9618_s25_qp_13_sc_1.c.i

Question

A character set is used to represent characters in a computer. Identify and describe one character set.

Answer — 1 mark for identification, 1 for a matching description

• ASCII — 7/8 bits per character // represents 128/256 characters // represents all characters from the Latin alphabet
• UNICODE — 8/16/32 bits per character // represents 256/65 536+ characters // represents all characters in all languages
• The description must match the set named — describing Unicode after naming ASCII scores 1

Latest: 9618_w24_qp_13_sc_6.a

Question

Complete the table by identifying the number of bits each character set allocates to each character: ASCII, extended ASCII, Unicode.

Answer — 1 mark for all three correct

• ASCII → 7
• extended ASCII → 8
• Unicode → 16 / 32
• All-or-nothing — two right and one wrong scores zero

Latest: 9618_s24_qp_13_sc_1.d.i

Question

State the number of characters that can be represented by the ASCII character set and by the extended ASCII character set.

Answer — 1 mark each

• ASCII = 128 // 2⁷
• Extended ASCII = 256 // 2⁸

Latest: 9618_s21_qp_12_sc_6.a

Question

The ASCII value for the character ‘h’ has the denary value 104. Write the BCD value and the hexadecimal value for ‘h’.

Answer — 1 mark each

• BCD: 1 → 0001, 0 → 0000, 4 → 0100 = 0001 0000 0100
• Hexadecimal: 104 ÷ 16 = 6 remainder 8 = 68
• More: Unicode ɮ = 0010 0111 0110 1110 → denary 10 094 · Unicode ∑ = hex 2140 → denary 8512 · Unicode 1 = denary 49 → hex 31 · Unicode 5 → denary 53
• Character-set questions are usually conversion questions in disguise

Latest: 9618_w25_qp_12_sc_7.c.i/c.ii (also s25_qp_11_sc_3.b.ii/b.iii, s21_qp_12_sc_6.c)

Question

Use the table of words and denary values to find the words corresponding to the binary values 00111000, 00111100 and 00111110.

Answer — 1 mark for correct words in the correct order

• 0011 1000 = 56 → Science
• 0011 1100 = 60 → is
• 0011 1110 = 62 → Amazing!
• Convert each binary value to denary first, then look it up

Latest: 9618_w25_qp_12_sc_6.b

Question

Define the term character set.

Answer — 1 mark from

• The symbols that the computer recognises / uses
• A list of characters recognised by the computer hardware and software
• … each one given a unique binary code

Latest: 9608_s18_qp_12_sc_4.b.i. 9618 has never asked for the definition itself — only for how one works.

Question

Give two disadvantages of using ASCII code.

Answer — 1 mark each, any two

• Only 128 / 256 characters can be represented
• It uses values 0 to 127 (or 255 if extended) / one byte
• Many characters used in other languages cannot be represented
• In extended ASCII, the characters from 128 to 255 may be coded differently in different systems
• That last point — extended ASCII is not consistently standardised — appears in no 9618 mark scheme

Latest: 9608_w16_qp_13_sc_8.c.i

Question

Describe how Unicode is designed to overcome the disadvantages of ASCII.

Answer — 1 mark each, any two

• Uses 16, 24 or 32 bits / two, three or four bytes
• Unicode is designed to be a superset of ASCII
• Designed so that most characters in other languages can be represented
• Superset is the sharp version of 9618’s “ASCII is a subset of Unicode”

Latest: 9608_w16_qp_13_sc_8.c.ii

Question

The ASCII code for ‘A’ is 41 in hexadecimal. Calculate the ASCII code in hexadecimal for ‘Z’. Show your working.

Answer — 1 mark for working, 1 for the answer

• Working: code for Z = code for A + 25₁₀ (A is the 1st letter, Z the 26th)
• 25₁₀ = 19₁₆
• 41₁₆ + 19₁₆ = 5A₁₆
• This works only because character sets are ordered logically — each letter’s code is one more than the last

Latest: 9608_s18_qp_12_sc_4.b.iii (also 9608_s19_qp_12_sc_3.d.iii, Unicode ‘G’ = 0047 so ‘D’ = 0044)

Question

Explain what is meant by saying a character set is logically ordered, and give one benefit.

Answer — 5 points for 3 marks

• Codes are assigned in sequence, so the code for ‘B’ is one more than the code for ‘A’
• … which means characters can be sorted alphabetically by comparing their codes directly
• … and a character’s code can be calculated from another’s rather than looked up
• In ASCII, only the sixth bit differs between a capital and its lower-case letter (A = 0100 0001, a = 0110 0001)
• … so case conversion is a single bit operation, which is fast

Inference: 9608 exploits the ordering in a calculation question, but neither series has ever asked about the property itself — even though it is why character sets are designed this way.

Question

Compare the ASCII and Unicode character sets in terms of bits, characters, uses, benefits and drawbacks.

Answer — 6 points for 4 marks

• Bits: ASCII 7 · extended ASCII 8 · Unicode minimum 16
• Characters: 128 · 256 · 65 536+
• Covers: ASCII the standard English keyboard; extended ASCII adds operators and symbols such as ©; Unicode all major world languages and emoji
• Benefit of ASCII: uses far less storage space
• Benefit of Unicode: represents all common characters worldwide, including special characters
• Drawback of Unicode: uses considerably more storage space
• The first 128 Unicode code points are identical to ASCII, which is what makes it backward compatible

9618’s own comparison question carries 3; the full table justifies 4.

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1.2 Multimedia

1.2.1 How bitmap data is encoded

Question

Describe how the data for a bitmapped image is encoded.

Answer — 1 mark each

• The image is made of pixels and each pixel has one colour
• Each colour has a unique binary code
• The code for the colour of each pixel is stored in sequence
• Identical structure to the character-set and sound answers — learn the shape once

Latest: 9618_s24_qp_12_sc_2.d.i

Question

Complete the table by defining the image terms: pixel, colour depth, file header.

Answer — 1 mark for each correct definition

• Pixel — the smallest part of the image // one square or dot of one colour // the smallest addressable element in an image
• Colour / bit depth — the number of bits per pixel // the number of bits used to represent each colour // determines the number of colours that can be represented
• File header — stores data about the image file / metadata
• Drawing list (if asked alongside) — all the drawing objects in an image // a list storing the commands required to draw each object

Latest: 9618_s23_qp_11_sc_1.a (also w24_qp_12_sc_7.a.i, s21_qp_11_sc_1.a.i)

Question

Complete the statements about bitmap images by writing the missing words.

Answer — 1 mark per correctly completed term

• The bit depth of a bitmap image is the number of bits that are used to store each pixel
• Metadata about the image is stored in the header of the file

Latest: 9618_w22_qp_13_sc_2.e

Question

Complete the table by writing the answer for each statement about a bitmapped image.

Answer — 1 mark for each correct answer

• The term for the smallest element that makes up an image → pixel
• The largest number of different colours representable with a bit depth of 8 bits → 256 // 2⁸
• The term for the dots per inch (dpi) when an image is displayed → screen resolution
• The third row is the only place screen resolution has ever been the answer in 9618

Latest: 9618_s25_qp_11_sc_3.a

Question

Colour depth and image resolution are both included in the file header of a bitmap image. Identify two other items that could be included.

Answer — 1 mark each to max 2

• Confirmation that it is a bitmap // the file type
• The compression type used
• The location / offset of the data within the file
• The dimensions, e.g. 100 × 100 pixels
• Colour depth and image resolution are excluded by the question

Latest: 9618_s23_qp_11_sc_1.b.i

Question

Explain why the actual file size of a bitmap image may be larger than the one you calculated.

Answer — 1 mark per bullet, max 2

• The file will contain metadata
• The file will have a header
• … which stores the file type, dimensions, colour depth and compression type, none of which the formula counts
• This is why every file-size question asks for an estimate

Latest: 9618_w25_qp_11_sc_7.d.ii

Question

Explain the difference between image resolution and screen resolution.

Answer — 5 points for 3 marks

• Image resolution is a property of the file — the number of pixels making up the image, width × height
• … so it directly affects the file size
• Screen resolution is a property of the display — the number of pixels the monitor can show
• … so changing it has no effect on the file size of the image
• If the image resolution exceeds the screen resolution, the image is scaled down to fit, and detail is not visible

Inference: both terms are named in the syllabus. Image resolution is examined constantly; screen resolution appears once as a one-word answer and once as a distractor. The distinction has never been the subject of a question.

Question

Explain what is meant by pixel density and why it affects perceived image quality.

Answer — 5 points for 3 marks

• Pixel density is the number of pixels per inch (PPI) of a display
• It depends on the screen resolution and the physical size of the screen
• … so a large screen and a small screen with the same resolution have different densities
• A 65″ 4K television works out at roughly 68 PPI; a modern smartphone exceeds 300 PPI
• At low density and close viewing distance the pixel grid becomes visible and fine detail is lost

SME’s extension of the screen-resolution row that 9618 does examine; it explains why “dpi” is the credited term.

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1.2.2 Calculating bitmap file size

Question

A bitmap image has a resolution of 1000 pixels wide by 2000 pixels high. The colour depth is 16 bits. Calculate an estimate of the file size in megabytes. Show your working.

Answer — 1 mark for the working, 1 for the answer

• Working: (1000 × 2000 × 16) ÷ (8 × 1000 × 1000)
• … // (1000 × 2000 × 2) ÷ (1000 × 1000)
• Answer: 4 megabytes
• Others: 2 000 000 pixels × 16 bits ÷ (8 × 1000 × 1000) = 4 MB
• … 1500 × 3000 × 8 bytes ÷ 1000 ÷ 1000 = 36 MB — note no ÷ 8, the depth is in bytes

Latest: 9618_w25_qp_12_sc_6.d (also s25_qp_13_sc_1.a.i, s23_qp_11_sc_1.b.ii)

Question

A photograph has a resolution of 4000 pixels wide by 3000 pixels high. The bit depth is 4 bytes. Calculate an estimate for the file size in megabytes. Show your working.

Answer — 1 mark for the working, 1 for the answer

• Working: 4000 × 3000 × 4
• Answer: 48 MB
• There is no ÷ 8 — the depth is already in bytes, so the product is already in bytes
• 48 000 000 bytes ÷ 1 000 000 = 48 MB
• The single most common slip in this bullet: dividing by 8 anyway gives 6 MB and loses the answer mark

Latest: 9618_s24_qp_13_sc_2.a

Question

Calculate the file size of a bitmap image with an image resolution of 2048 × 1024 pixels and a bit depth of 16 bits. Give your answer in kibibytes. Show your working.

Answer — 1 mark for the answer, 1 for the working

• Working: (2048 × 1024 × 16) ÷ (8 × 1024)
• Answer: 4096 kibibytes
• Kibibytes → divide by 1024, not 1000
• Also: 512 × 2048 × 8 ÷ (8 × 1024) = 1024 kibibytes // 2¹⁰ kibibytes — “a maximum of 256 colours” means a bit depth of 8

Latest: 9618_w23_qp_13_sc_2.b (also w25_qp_11_sc_7.d.i)

Question

The image is scanned with an image resolution of 1024 × 512 pixels and a colour depth of 8 bits per pixel. Calculate an estimate for the file size in mebibytes. Show your working.

Answer — 1 mark per working bullet, 1 for the answer

• Working: 1024 × 512 = 524 288 pixels/bytes (at 8 bits = 1 byte per pixel)
• Working: 524 288 ÷ 1024 ÷ 1024
• Answer: 0.50 mebibytes
• Also: (2048 × 1024 × 10) ÷ (8 × 1024 × 1024) = 2.5 mebibytes
• Mebibytes → divide by 1024 twice

Latest: 9618_s21_qp_11_sc_1.a.ii (also w23_qp_12_sc_6.c)

Question

A video is made of many bitmap images called frames. 30 frames are recorded every second. Each frame is 4000 pixels wide by 3000 pixels high, using 16-bit colour depth. Calculate an estimate for the file size for one second of video in gigabytes. Show your working.

Answer — 1 mark for the working, 1 for the answer

• Working: (4000 × 3000 × 30 × 16) ÷ (8 × 1000 × 1000 × 1000)
• … // (4000 × 3000 × 30 × 2) ÷ (1000 × 1000 × 1000)
• Answer: 0.72 gigabytes
• A video is just a sequence of bitmaps — the frame rate is one more multiplier

Latest: 9618_w25_qp_13_sc_7.b

Question

A bitmap image of 800 × 600 pixels has a file size of 1.44 MB. Calculate the bit depth used. Show your working.

Answer — 5 points for 3 marks

• Total pixels = 800 × 600 = 480 000
• File size in bits = 1.44 × 1 000 000 × 8 = 11 520 000 bits
• Bit depth = 11 520 000 ÷ 480 000 = 24 bits
• … which is true colour, 2²⁴ = 16 777 216 colours
• The same formula rearranged — and the same marking structure, working then answer

Inference: every 9618 question gives resolution and depth and asks for the size. The inverse — find the depth, or find how many images fit in a given capacity — has never been set, though it is the standard way to raise difficulty.

Question

An audio message is recorded with a sampling rate of 50 kHz and a sampling resolution of 16 bits. The recording is 20 minutes long. Calculate the file size in megabytes. Show your working.

Answer — 1 mark for the answer, 1 for the working

• Formula: sampling rate × sampling resolution × duration in seconds
• Working: 50 000 × (20 × 60) × 16 bits
• = 50 000 × 1200 × 16 = 960 000 000 bits
• = 120 000 000 bytes = 120 000 kilobytes = 120 megabytes
• Convert the duration to seconds first — minutes is the usual trap

Latest: 9618_w22_qp_13_sc_1.b. Filed under Data Representation in the Legend, not Multimedia, but it is the same discipline as the bitmap formula.

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1.2.3 Effects of changing bitmap elements

Question

A second photograph is taken with a lower bit depth. Explain the effect of decreasing the bit depth on the image and on the image file.

Answer — 1 mark each, two per half, 4 marks

• Image: there will be fewer shades of colour available
• … so the image does not match the original, as detail is lost
• Image file: fewer bits are used to store each pixel
• … so less data is stored, therefore the file size is reduced
• Two marks are reserved for each half — four points about the image alone scores 2

Latest: 9618_s25_qp_13_sc_1.a.ii

Question

State what is meant by the bit depth of a bitmap image and explain how changing the bit depth affects the image.

Answer — 1 mark for the definition + 1 per explanation bullet

• Definition: the number of bits used to represent each colour
• Explanation: an increase means the image has a greater range of colours // a decrease means a smaller range
• Explanation: an increase makes the image closer to the original / more realistic // a decrease makes it less like the original / less realistic

Latest: 9618_w23_qp_11_sc_1.b

Question

Explain why changing the image resolution will affect the image quality and the file size.

Answer — 1 mark for each correct explanation

• Image quality: decreasing the resolution means details are lost because there are fewer pixels
• … // increasing means the image is more detailed because there are more pixels
• File size: decreasing the resolution decreases the file size because there are fewer pixels therefore less data
• … // increasing increases it because there are more pixels therefore more data
• Each half must carry its because — the bare statement scores nothing

Latest: 9618_w24_qp_12_sc_7.a.ii

Question

Describe the impact of increasing the image resolution on the quality of a bitmap graphic.

Answer — 1 mark for each bullet

• More pixels can be stored / are available
• The image is sharper / less pixelated

Latest: 9618_w23_qp_13_sc_2.a

Question

Identify two elements of a bitmap image that can be changed to reduce its file size.

Answer — 1 mark each to max 2

• Colour / bit depth
• Image resolution
• Only these two are credited — offering compression or screen resolution scores nothing
• Related: one drawback of increasing the bits per pixel is simply increased file size (w24_qp_13_sc_6.b.ii)

Latest: 9618_s24_qp_13_sc_2.c

Question

Tick one box in each row to identify the effect of each action on the image file size.

Answer — 1 mark for one or two correct rows, 2 marks for all three

• Change the colour depth to 16 bits per pixel (from 24) → decreases the file size
• Change the screen resolution to 1366 × 768 → no change to the file size
• Change the colour of the rectangle from black to red → no change to the file size
• Screen resolution is a property of the display, not the file
• Changing which colour is used does not change how many bits store it

Latest: 9618_w22_qp_12_sc_8.a

Question

A company needs images for a website that must load quickly on mobile connections. Recommend a suitable image resolution and bit depth, and justify your choices.

Answer — 2 marks per choice (recommendation + justification), 4 marks

• A lower image resolution, matched to the size the image is displayed at
• … because extra pixels beyond the display size add file size without visible benefit, and a smaller file loads faster over a mobile connection
• A moderate bit depth (e.g. 24-bit for photographs, 8-bit for simple graphics)
• … because photographs need a wide range of colours to look realistic, but a logo or icon with few colours needs far fewer bits per pixel
• The justification must be tied to the stated purpose — generic “smaller is better” scores nothing

Inference: every question asks what happens when a value changes; none asks the candidate to recommend and justify — the format used constantly for storage devices in Chapter 3, and already used in 1.2.5 for bitmap vs vector.

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1.2.4 How vector graphic data is encoded

Question

Complete the table by defining the vector graphic terms: drawing list, drawing object, property.

Answer — 1 mark for each correct definition

• Drawing list — all the drawing objects in an image // a list that stores the commands / descriptions / mathematical equations required to draw each object
• Drawing object — a component created using a formula
• Property — an attribute of a drawing object // data about a shape // defines one aspect of the appearance of a drawing object

Latest: 9618_w24_qp_12_sc_7.b (also w23_qp_11_sc_1.a, s23_qp_11_sc_1.a)

Question

Describe the contents of a vector graphic drawing list.

Answer — 1 mark each to max 2

• A list of the objects in the drawing
• A list that stores the command / description / equation required to draw each object
• The properties of each object, e.g. the fill colour, the line weight or colour

Latest: 9618_s24_qp_12_sc_2.d.ii

Question

Complete the table by writing a description of each vector graphic term and giving an example for the logo shown.

Answer — 1 mark for each description, 1 for each valid example

• Property — data about the shapes // defines one aspect of the appearance of the drawing object
• … example: black line // white fill // black fill // solid line // font of the letter // colour of the triangle
• Drawing list — the list of shapes involved in an image // a list that stores the command or description required to draw each object
• … example: triangle // capital letter R // rectangle // line
• The example must be read off the image given — a generic example scores nothing

Latest: 9618_w21_qp_12_sc_5.a

Question

Draw one line from each vector graphic term to its most appropriate description.

Answer — 2 marks for all 3 correct, 1 mark for 1 correct

• Drawing list → data required to create all components in the graphic
• Drawing object → a component created using a formula
• Property → defines one characteristic of a component
• The lines cross — the terms are not listed in the same order as the descriptions

Latest: 9618_w23_qp_11_sc_1.a

Question

Explain why a vector graphic can be enlarged without any loss of quality, whereas a bitmap image cannot.

Answer — 5 points for 3 marks

• A vector file stores mathematical equations and coordinates, not pixels
• … so when the image is enlarged the shapes are recalculated at the new size
• … and the lines are redrawn at full sharpness, so no detail is invented or lost
• A bitmap stores a fixed grid of pixels, so enlarging makes each pixel bigger
• … which is why the image pixelates — the detail was never in the file to begin with

Inference: credited as a bare point inside the comparison questions (“recalculated and does not pixelate”), but the mechanism has never been the subject of its own question.

Question

Describe what is stored in a vector graphic file.

Answer — 5 points for 3 marks

• Only the mathematical equations and points needed to create the image
• … e.g. for a circle, the centre point (x, y) and the radius — nothing else
• A drawing list, held in the file header
• … containing the commands to create each object, the properties of each object, and the relative position of each object
• No dimensions are defined, which is why scaling up causes no loss of quality

The relative position point is SME-only — 9618 credits commands and properties repeatedly but never mentions position, even though it is what makes a drawing list reproducible.

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1.2.5 Justifying bitmap or vector

Question

Describe two differences between a vector graphic and a bitmap image.

Answer — 1 mark per bullet to max 2 for each difference

• Composition: a bitmap is made up of pixels // colours stored for individual pixels
• … a vector graphic stores a set of instructions about how to draw the shape
• Scaling: when a bitmap is enlarged the pixels get bigger and it pixelates
• … when a vector is enlarged it is recalculated and does not pixelate
• File size: bitmap files are usually bigger, because of the need to store data about each pixel
• … vector graphics have a smaller file size because they contain just the instructions
• Compression: bitmaps compress well, with significant reduction in file size; vector graphics do not compress well, because there is little redundant data

Latest: 9618_w21_qp_12_sc_5.b.i

Question

State two benefits of creating a vector graphic instead of a bitmap image.

Answer — 1 mark each to max 2

• Can be enlarged without pixelation / loss of quality
• Individual components of the image can be edited
• Generally a smaller file size

Latest: 9618_w22_qp_12_sc_8.b. The only 9618 question under this bullet.

Question

Describe two drawbacks of using a bitmapped image for a logo instead of a vector graphic.

Answer — 1 mark per drawback, 1 for its expansion, max 2 each

• A bitmap file is likely to take up more storage space
• … because the colour of each pixel needs to be stored
• A bitmap cannot be enlarged // is difficult to use in different types of document
• … without the image pixelating
• A bitmap would be more difficult to edit
• … because each pixel would need to be edited separately
• The editability drawback appears in no 9618 mark scheme

Latest: 9608_s21_qp_11_sc_2.c

Question

A company has created a logo for its website as a vector graphic. Describe two reasons why a vector graphic is a sensible choice for the logo.

Answer — 1 mark per bullet, max 2 per reason

• Smaller file size
• … so it can be transferred / downloaded more quickly
• Enlarges without pixelation
• … because it needs to be used on different screens / devices / resolutions
• Also credited: it needs to be large for signs without pixelating · a smaller file size reduces storage when the logo is stored on many documents

Latest: 9608_w18_qp_12_sc_1.c (also s18_qp_11_sc_2.d, s20_qp_13_sc_7.c, s20_qp_12_sc_1.b.ii)

Question

A wildlife photographer stores photographs on a website. Justify the use of bitmap images rather than vector graphics.

Answer — 2 points + expansions, 4 marks

• A photograph has continuous tone and millions of subtly different colours
• … which cannot be described as a set of shapes and equations, so a vector graphic could not reproduce it
• A photograph has no discrete objects to store in a drawing list
• … every pixel is independent, which is exactly what a bitmap stores
• A camera sensor produces pixel data directly, so a bitmap needs no conversion
• Bitmaps also compress well (lossy, e.g. JPEG), so the large file size is manageable

Inference: every question in both series runs the argument one way — why vector beats bitmap for a logo. The reverse is half of what the bullet says and has never been asked.

Question

State three questions that should be considered when choosing between a bitmap image and a vector graphic.

Answer — 5 points for 3 marks

• Does the image need to be resized? — if yes, vector
• Does the image need to be drawn to scale? — if yes, vector
• Does the image need to look real? — if yes, bitmap
• Vector suits logos, icons and text graphics; bitmap suits photographs and detailed images
• File types: vector .svg, .ai, .eps · bitmap .jpg, .png, .bmp, .gif

SME’s decision framework; 9618 marks justification questions at up to 2 and 9608 at up to 4.

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1.2.6 How sound is represented and encoded

Question

Describe how sound is represented in a computer.

Answer — 1 mark each

• The amplitude is recorded a set number of times a second
• Each instance of an amplitude is given a corresponding binary number
• The binary number of each amplitude is saved in sequence
• Same three-part shape as the bitmap and character-set answers

Latest: 9618_s23_qp_13_sc_3.c.i

Question

Explain how an analogue sound wave is converted into digital data.

Answer — 1 mark per bullet, max 2

• The value / magnitude / size of the analogue sound wave is measured a set number of times each second / at set intervals
• Each sample / reading / measurement is given a binary number and stored in sequence

Latest: 9618_w24_qp_13_sc_6.c.i

Question

Complete the table by giving the term for each description about sound representation.

Answer — 1 mark for each correct term

• The number of times the amplitude is measured per time interval → sampling rate
• The number of bits used to store each amplitude measurement → sampling resolution
• The type of sound wave before it is recorded by a computer → analogue

Latest: 9618_s25_qp_13_sc_1.b

Question

Draw one line from each term to its most appropriate description: sampling, sampling rate, sampling resolution.

Answer — 1 mark for 1 correct line, 2 marks for all 3

• Sampling → taking measurements at regular intervals and storing the values
• Sampling rate → the number of samples taken per second
• Sampling resolution → the number of bits used to store each sample
• The lines cross — sampling is not the first description

Latest: 9618_w23_qp_13_sc_1.b

Question

(i) State what is meant by sampling rate. (ii) State what is meant by analogue data.

Answer — 1 mark each

• Sampling rate — the number of samples taken per unit time / per second
• Analogue data — data values that are continuously changing // variable // can take any value
• … as opposed to digital data, which takes only discrete values

Latest: 9618_w22_qp_11_sc_1.d.i and 9618_w23_qp_13_sc_1.a

Question

Complete the table by writing the definitions for each term: sampling, sampling resolution, sampling rate.

Answer — 1 mark per correct definition

• Sampling — measuring the amplitude of the wave at regular / set time intervals
• Sampling resolution — the number of bits used to represent each sample
• Sampling rate — the number of samples taken per unit of time
• 9618 asks these as a matching exercise; 9608 requires them written out

Latest: 9608_w21_qp_11_sc_2.a (also s20_qp_12_sc_2.a)

Question

A sound track has a sampling rate of 88.2 kHz and a sampling resolution of 32 bits. State what each of these means.

Answer — 1 mark per bullet

• 88.2 kHz — the sound wave is sampled 88 200 times per second
• 32 bits — each sample is stored as a 32-bit binary number
• Reading the units off an actual specification is never asked in 9618

Latest: 9608_s19_qp_13_sc_5.c

Question

A video is made of a sequence of images and a sound file. Describe how the images and the sound are encoded into digital form.

Answer — 1 mark per bullet, max 4; max 3 for image, max 3 for sound

• Images: stored as bitmaps; each image is made up of pixels
• … each pixel is a single colour, and each colour has a unique binary number
• … store the sequence of binary numbers for each image / frame
• Sound: measure the height / amplitude of the sound wave
• … a set number of times per second / at regular time intervals
• … each amplitude has a unique binary number, and the sequence of numbers is stored
• One question covering both encodings, which 9618 has never set

Latest: 9608_w19_qp_12_sc_6.d.i

Question

Describe two features of sound editing software that can be used to edit a sound file.

Answer — 1 mark for naming, 1 for describing, max 2 per feature

• Cut / delete — remove part of the sound file, e.g. background noise
• Copy and paste — replicate part of the sound
• Amplify — increase the volume of a section of sound
• Fading — change the volume of a section so it gets louder or quieter
• Change pitch — increase or decrease the frequency of a section
• Change sampling resolution — to change the accuracy of the sound or the file size
• Sound editing appears nowhere in the 9618 syllabus notes — background, not a likely question

Latest: 9608_w18_qp_11_sc_1.c (also s18_qp_12_sc_5.d, w19_qp_13_sc_2.b.iii)

Question

Explain how a sound file stored in binary is played back through a speaker.

Answer — 5 points for 3 marks

• The stored binary values are read back in sequence, at the same rate they were sampled
• Each value is converted into a voltage by a digital-to-analogue converter (DAC)
• … reconstructing an approximation of the original analogue waveform
• The varying voltage drives the speaker cone, which vibrates
• … producing sound waves in the air
• The reconstruction is only an approximation — quantisation errors from recording remain

Inference: every question describes recording. The reverse path is never asked, though SME describes it and the speaker is in the Chapter 3 syllabus.

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1.2.7 Impact of changing sampling rate and resolution

Question

Explain the reasons why increasing the sampling rate and the sampling resolution will improve the precision of a recording.

Answer — 1 mark each; max 2 for rate, max 2 for resolution

• Sampling rate: there are smaller ‘gaps’ in the sound wave // sound is recorded more often
• … the digital waveform is closer to the analogue waveform
• … the quantisation errors are smaller
• Sampling resolution: there are more bits per sample // a wider range of amplitudes can be stored
• … each binary amplitude is closer to the analogue amplitude
• … the digital waveform is closer to the analogue waveform, and quantisation errors are smaller
• Four points about the rate alone caps at 2

Latest: 9618_s23_qp_13_sc_3.c.ii

Question

Explain the impact of changing the sampling resolution on the accuracy of a sound recording.

Answer — 1 mark per bullet, max 3

• Increasing: the number of bits used for each sample is increased
• … there will be more values available to represent each sample // more amplitudes can be represented
• … each binary amplitude in the digital recording is closer to the analogue amplitude
• … quantisation errors are reduced
• … the digital soundwave is closer to the original analogue soundwave
• Decreasing: the exact mirror of each point above

Latest: 9618_w23_qp_12_sc_6.b

Question

Explain the effect of increasing the sampling rate on the accuracy of a sound recording.

Answer — 1 mark per bullet, max 2

• It improves the accuracy of the sound file
• … because the digital waveform more closely resembles the analogue waveform
• Quantisation errors are reduced
• It increases the amount of detail stored

Latest: 9618_w22_qp_12_sc_6.b.i

Question

(i) Explain the effect of increasing the sampling resolution on the sound file. (ii) Explain the effect of decreasing the sampling resolution on the file size.

Answer — 1 mark per bullet, max 2 each

• Increasing: increases the number of bits per sample // a larger range of values
• … which means the file size increases
• … makes the sound file more accurate // digital waveform closer to the original
• … smaller quantisation errors
• Decreasing: decreases the file size of the sound file
• … because fewer bits are used to store each sample

Latest: 9618_w22_qp_11_sc_1.d.ii and 9618_w22_qp_12_sc_6.b.ii

Question

(i) Describe the reasons why the sound is closer to the original when a higher sample rate is used. (ii) Describe the reasons why the file size increases when a higher sample rate is used.

Answer — 1 mark per bullet, 2 marks each part

• (i) Smaller time gaps between the samples
• … makes the digital sound wave more accurate
• … smaller quantisation errors
• (ii) More samples / data are taken or recorded
• … so more bits are stored altogether

Latest: 9618_w21_qp_11_sc_7.a.i and a.ii

Question

Draw two lines from each change to the impacts it has on the sound file.

Answer — 1 mark per correctly connected change box, max 3

• Increase the duration → the file size gets bigger · no change to the accuracy
• Increase the sampling rate → the file size gets bigger · the accuracy improves
• Decrease the sampling resolution → the file size gets smaller · the accuracy worsens
• Each change box needs two lines — one for size, one for accuracy

Latest: 9618_w25_qp_13_sc_1.a

Question

Tick one box in each row to identify the effect of each action on the accuracy of the recording.

Answer — 1 mark for 1–2 correct, 2 marks for all 3

• Change the sampling rate from 40 kHz to 60 kHz → accuracy increases
• Change the duration from 20 minutes to 40 minutes → accuracy does not change
• Change the sampling resolution from 24 bits to 16 bits → accuracy decreases
• The duration row is the trap: a longer recording is bigger, not more accurate

Latest: 9618_w22_qp_13_sc_1.a

Question

The user changes the sampling rate the microphone uses from 44.1 kHz to 88.2 kHz. Describe how this change will affect the performance of the video doorbell.

Answer — 1 mark each to max 3

• Data transmission to the user’s smartphone will take longer
• … because there is more data to transmit
• The secondary storage device will fill faster
• … so fewer videos can be stored long-term // videos are overwritten more often
• This is the applied, consequences version of the bullet — the answer must be about the device, not about waveforms

Latest: 9618_s24_qp_11_sc_2.d

Question

Define the term sampling rate. Explain how the sampling rate will influence the accuracy of the digitised sound.

Answer — 1 mark for the definition, 1 for the explanation

• Definition: the number of samples taken per unit time // the number of times the amplitude is measured per unit time
• Explanation: increasing the sampling rate will increase the accuracy / precision of the digitised sound
• … // increasing the sampling rate will result in smaller quantisation errors
• 9618 asks the definition and the effect in separate questions; 9608 combines them

Latest: 9608_s17_qp_13_sc_3.a (also s17_qp_11_sc_3.a)

Question

Define the term sampling resolution. Explain how the sampling resolution will affect the accuracy of the digitised sound.

Answer — max 2 for the definition, max 2 for the explanation, 3 marks

• Definition: the number of distinct values available to encode / represent each sample
• … specified by the number of bits used to encode the data for one sample
• … sometimes referred to as bit depth
• Explanation: a larger resolution means there are more values available to store each sample
• … it will improve the accuracy of the digitised sound // decrease the distortion of the sound
• … increased sampling resolution means a smaller quantisation error
• Two pieces of vocabulary are 9608-only: “distinct values available” and “distortion”

Latest: 9608_s17_qp_12_sc_3.a

Question

Explain what is meant by a quantisation error in a digital sound recording.

Answer — 5 points for 3 marks

• The sampling resolution gives only a fixed number of possible values (2ⁿ for n bits)
• The true amplitude of the wave at the moment of sampling usually falls between two of those values
• … so it must be rounded to the nearest available value
• The difference between the true amplitude and the stored value is the quantisation error
• Increasing the sampling resolution reduces it, because the available values are closer together

Inference: “quantisation errors” is a credited mark point in six separate 9618 mark schemes — the most-credited term in 1.2.7 — yet no question in either series asks what one is.

Question

State the effect of increasing the sampling rate, the sampling resolution and the duration on playback quality and file size.

Answer — 1 mark per row, 3 marks

• Sampling rate ↑ → more detail, better sound quality · more data, larger file size
• Sampling resolution ↑ → bigger range of amplitudes, better sound quality · more data per sample, larger file size
• Duration ↑ → larger file size, but no change to accuracy
• For reference, a typical audio CD: sampling rate 44.1 kHz, sampling resolution 16 bits, recorded in stereo

SME’s table; 9618’s own tick-table question (w22_qp_13_sc_1.a) marks exactly these three rows.

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1.3 Compression

1.3.1 The need for compression

Question

Explain the reasons why a video is compressed before it is transmitted using real-time bit streaming.

Answer — 1 mark each to max 4

• Video is data-intensive
• The file size needs reducing in order to reduce the amount of bandwidth used
• … and to reduce buffering
• This means people are not behind in the conversation
• … and people with lower bandwidth can still take part
• The highest-tariff compression question in the chapter, and the one most tied to Topic 2

Latest: 9618_s25_qp_11_sc_2.b.i

Question

Explain the benefits to the recipient of an email attachment being a compressed file.

Answer — 1 mark per bullet, max 3

• Less of the recipient’s storage space is used
• … so more work can be stored
• Transmission time is reduced
• … so the recipient does not have to wait as long for it to arrive
• Bandwidth usage is reduced
• … so other transmissions are not adversely affected
• Less data allowance is used on the email system
• Each benefit has its own consequence, and the consequence is a separate mark

Latest: 9618_w24_qp_11_sc_4.b.i

Question

Explain why a bitmap image is often compressed before it is attached to an email.

Answer — 1 mark per bullet, max 2

• Reduced bandwidth usage when transmitting the message
• Reduced transmission time from email client to email server
• Reduced storage space on the email
• Email accounts often have a maximum size for an attachment
• That last point is specific to email and is easily missed

Latest: 9618_w23_qp_11_sc_1.c

Question

Photographs are compressed before being uploaded to a web server. Explain the reasons why compressing the photographs will benefit the customers.

Answer — 1 mark each, max 3

• The customers will be able to download the photographs in less time
• … and they will take less of the customer’s bandwidth
• The photographs will take up less space on the customer’s storage medium
• … therefore the customers can store more images
• … and will have more space for other files
• Every point must be framed from the customer’s side, not the server’s

Latest: 9618_s23_qp_11_sc_1.c.i

Question

A program is distributed by downloading its files from a web server. Explain the reasons for compressing the files.

Answer — 1 mark per bullet, max 2

• To reduce the time it takes to download the files from the web server
• … // to upload them to the server in the first place
• To reduce the amount of storage space used on the web server // on the user’s device
• Related: compressing a sound file before emailing it — reduces the file size · faster to transmit / download · the original file is too large for email storage (w21_qp_11_sc_7.b.i)

Latest: 9618_w23_qp_13_sc_5.b.i

Question

The bitmap video is not compressed before transmission to the VR headset. Give two reasons why the video does not need to be compressed.

Answer — 1 mark each to max 2

• A dedicated connection to the headset // not sharing bandwidth
• An already fast connection that can transmit the data without slowing
• The video may already be a small file size and does not need further reduction
• The video is not saved, so storage is not an issue in the headset
• The inverted question — the marks are for reasons compression is unnecessary, not for its drawbacks

Latest: 9618_s24_qp_12_sc_2.d.iii

Question

State three drawbacks of compressing a file.

Answer — 5 points for 3 marks

• Compression and decompression take processing time
• … so there is a delay before the file can be used, and extra CPU load on both devices
• Lossy compression permanently destroys data, so the original cannot be recovered
• … and repeatedly compressing the same file degrades it further each time
• An already-compressed file cannot usefully be compressed again — the redundancy has gone

Inference: every question in both series asks for the benefits. The costs are never asked — the closest is “why does this video not need compressing”, which is argued from bandwidth rather than from the cost of compressing.

Question

Name three common compression formats and describe briefly how each reduces file size.

Answer — 1 mark per format with its method, 3 marks

• MP3 — audio; reduces a file by up to 90% using perceptual music shaping, removing frequencies outside human hearing and quieter sounds masked by louder ones; lossy
• MP4 — multimedia (audio, video, photos, animations); the standard for streaming, keeping file size small without noticeable loss
• JPEG — the standard lossy bitmap format; it creates a new file, so the original can no longer be used
• SVG — vector files are XML text, which is why they can be compressed at all (as .svgz)

Named formats are not required by the syllabus, but they are the obvious “give an example” answers.

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1.3.2 Lossy and lossless, and justifying a method

Question

A sound file is compressed by reducing the sampling rate. State whether this is lossless or lossy compression. Justify your choice.

Answer — 1 mark for the correct answer with justification

• Type of compression: lossy
• Justification: there will be fewer samples per second, so data will be permanently lost
• … // there will be fewer samples per second, so the original sound cannot be re-created
• The justification carries the mark — the label alone scores nothing

Latest: 9618_w25_qp_12_sc_6.a

Question

Identify whether the lossy or lossless compression method is more appropriate for real-time bit streaming. Justify your answer.

Answer — no mark for the choice; 1 mark each to max 3

• (Lossy is the expected answer)
• Reduces file size more than lossless
• … so significantly less bandwidth / data is needed
• … so buffering is reduced even more than with lossless
• Data can be removed which cannot be seen
• … reducing quality without impacting the experience
• … for example, the resolution of the video can be reduced // the sample rate of the audio can be reduced

Latest: 9618_s25_qp_11_sc_2.b.ii. Ticking the box and writing nothing scores zero.

Question

A real-time video of a music concert needs to be streamed to subscribers. Tick one box to identify the most appropriate type of compression and justify your answer.

Answer — 1 mark per bullet, max 3; both choices credited if justified

• Lossy (ticked): loss of quality will not be noticed
• … needs to be viewed in real time, so less bandwidth is needed if the file size is smaller
• … smaller file sizes reduce buffering, so the video plays more smoothly
• … viewers may watch on different devices, so may not need high quality resolution
• Lossless (ticked): the original recording may not have been made in high resolution
• … could be streaming to high bandwidth devices
• … the reduction in file size is sufficient for the receiving device // viewers do not want any loss of quality
• Unusually, either tick can score full marks — the justification decides it

Latest: 9618_w23_qp_12_sc_6.a

Question

A bitmap image can be compressed using lossy compression. Explain the reasons why lossy compression is often suitable for a bitmap image.

Answer — 1 mark per bullet, max 2

• The change may not be noticeable // data removed is usually not noticed by the human eye
• … for example, changes in shade or detail
• It produces a larger decrease in file size compared to lossless // lossy decreases file size considerably

Latest: 9618_w24_qp_13_sc_6.b.iii

Question

Give three benefits of a photograph being compressed using lossy compression instead of lossless compression.

Answer — 1 mark each to max 3 — every point must be comparative

• The file takes less storage space on the web server than if lossless was used
• The file is faster to upload / download to and from the server than if lossless was used
• The file uses less bandwidth to transmit than if lossless was used
• The file consumes less data allowance than if lossless was used
• Dropping “than if lossless was used” turns each point into a generic benefit of compression and loses the mark

Latest: 9618_s24_qp_13_sc_2.b.i

Question

RLE is an example of lossless compression. Explain why lossless compression is more appropriate than lossy compression for a text file.

Answer — 1 mark for each bullet

• All the data is required // no data can be lost
• … otherwise the text file will be corrupted / not make sense

Latest: 9618_w22_qp_12_sc_8.c.ii

Question

Define what is meant by lossy compression and by lossless compression.

Answer — 1 mark each

• Lossy — data is permanently removed to reduce the file size; the process is irreversible, so the original file cannot be recovered
• Lossless — the data is encoded rather than discarded, by finding patterns and repetition; the process is reversible, so the file can be restored exactly to its original state
• Lossy achieves a much greater reduction; lossless a smaller one

Inference: both terms are used in every question in this sub-topic, and neither has ever been asked for as a definition in 9618.

Question

A recording studio archives master audio recordings. Identify the more appropriate compression method and justify your answer.

Answer — 5 points for 3 marks (lossless)

• The recording is a master that will be edited further
• … and each lossy compression would permanently remove data, degrading it with every save
• No loss of quality is acceptable in a professional recording
• … and lossless is reversible, so the exact original waveform can be restored
• Storage is not the constraint here — the archive is not being streamed in real time

Inference: the justification questions cover lossy for video, lossy for photographs and lossless for text. Lossless for audio — the fourth combination — is never set, despite sound being examined heavily elsewhere in the topic.

Question

Describe how lossy compression works on a photograph and how lossless compression works on a document.

Answer — 6 points for 4 marks

• Lossy on a photograph: groups similar colours together
• … reducing the number of colours in the image without compromising the overall quality
• … the data removed is permanently lost, but the difference is hard to see
• Lossless on a document: uses algorithms to analyse the contents for patterns and repetition
• … encoding those repetitions more compactly rather than discarding anything
• … opening the file reverses the algorithm and returns the data to its original state
• Lossless is applied automatically to formats such as DOCX and PDF

SME’s worked explanation; 9618 marks “describe a method” questions at 2–4.

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1.3.3 How text, bitmap, vector and sound files are compressed

Question

Identify one lossless method of compressing an image.

Answer — 1 mark

• Run-Length Encoding (RLE)

Latest: 9618_w24_qp_12_sc_7.a.iii

Question

Identify one method of lossless compression that can be used to compress the image and describe how the method will reduce the file size.

Answer — 1 mark for naming the method, 1 per description bullet to max 2

• Run-length encoding
• Replaces sequences of the same colour pixel
• … with the colour code and the number of identical pixels
• The two words the mark scheme bolds are sequences and same colour

Latest: 9618_s21_qp_11_sc_1.b

Question

Explain how a bitmap image is compressed using run-length encoding (RLE).

Answer — 1 mark per bullet, max 2

• Sequences of consecutive identical colours / pixels
• … are stored as the colour value and the number of times it occurs consecutively
• The two credited words are consecutive and repeating — “the same colours are stored once” is not enough

Latest: 9618_w25_qp_11_sc_7.d.iii (also s24_qp_13_sc_2.b.ii)

Question

Describe one lossless method of compressing a text file.

Answer — 1 mark per bullet, max 3

• Run-Length Encoding // RLE
• Repeated sequences of the same characters are replaced by
• … a single copy of the character
• … and a count of the number of characters
• The four-bullet structure is worth copying exactly — naming it is one mark, and the mechanism is two more

Latest: 9618_w24_qp_11_sc_4.b.ii (also w22_qp_11_sc_7.c)

Question

The table shows compressed and uncompressed values for parts of an image file, where each colour is represented by a hexadecimal value. Complete the table.

Answer — 1 mark for each correctly completed part

• Given: EA F1 F1 F2 F2 F2 EA → 1EA 2F1 3F2 1EA
• Backwards: 2AB 2FF 11D 167 → AB AB FF FF 1D 67
• Forwards: 32 32 80 81 81 → 232 180 281
• The question runs in both directions — expansion must be as fluent as compression
• Note the count comes first, then the colour value

Latest: 9618_w22_qp_12_sc_8.c.i

Question

An image can be compressed using run-length encoding (RLE). Explain the reasons why RLE may not reduce the file size of a bitmap image. Give one example in your answer.

Answer — 1 mark each to max 2 for the explanation + 1 for an image-related example

• RLE stores a colour and the number of times it occurs consecutively
• An image may not have many sequences of the same colour
• It would need to store each colour and then the count / number 1, which adds data
• Example: Red-Green-Blue would become Red 1 Green 1 Blue 1
• The sharpest question in the sub-topic — and the only one that tests the limits of the method

Latest: 9618_s23_qp_11_sc_1.c.ii

Question

Describe two lossy methods that can be used to compress an image.

Answer — 1 mark per bullet to max 2 for each method

• Reduce bit depth
• … reduces the number of bits per colour / pixel, which means each pixel has fewer bits
• Reduce colour palette // reduce the number of colours
• … fewer colours mean fewer bits needed to store each colour
• Reduce image resolution
• … fewer pixels per unit measurement means less binary to store
• Three methods are credited, but only two can score — each needs its expansion

Latest: 9618_w21_qp_12_sc_5.b.ii

Question

Describe one method of compressing a sound file using lossy compression.

Answer — 1 mark for the point, 1 for a matching expansion

• Decrease the sample rate
• … fewer samples / readings / measurements stored per second // fewer bits per second stored
• Decrease the sample resolution
• … fewer bits per sample / reading / measurement // each sample has fewer bits
• Sound outside the set / human hearing range is removed
• … fewer measurements are stored // decreases the number of possible binary values so fewer bits are stored

Latest: 9618_w24_qp_13_sc_6.c.ii

Question

Describe how lossless compression can compress a sound file.

Answer — 1 mark per bullet, max 2

• Reduce the amplitude to only the range used
• … limited amplitudes mean fewer bits per sample
• Run-length encoding
• … where consecutive sounds are the same, record the binary value of the sound and the number of times it repeats
• Record the changes instead of the actual sounds
• That last method — storing deltas — is credited here and nowhere else in the chapter

Latest: 9618_w21_qp_11_sc_7.b.ii

Question

Explain how a vector graphic file can be compressed, and why it compresses less well than a bitmap.

Answer — 5 points for 3 marks

• An SVG file is an XML text file, so it can be compressed with the same lossless text methods as any document
• … producing a compressed .svgz file that can be restored exactly
• A vector image can also be simplified by reducing the number of objects or control points
• … which is lossy, because the removed detail cannot be recovered
• It compresses less well than a bitmap because a drawing list contains little redundant data
• … whereas a bitmap stores long runs of identical pixels, which is exactly what RLE exploits

*Inference: the bullet names “a text file, bitmap image, vector graphic and sound file”. Text, bitmap and sound each have multiple 9618 questions; vector graphics have none, in either series. The only trace is one line inside a comparison mark scheme. The clearest gap in 1.3.

Question

The text “AAAABBBCCDAA” is compressed using run-length encoding. Show the compressed form, and show how the first pair would be stored in binary.

Answer — 5 points for 3 marks

• Compressed form: 4A 3B 2C 1D 2A
• … four A’s, three B’s, two C’s, one D, two A’s
• The count is stored in a fixed-size binary field (e.g. 7 or 8 bits)
• The character is stored using its ASCII value (7 bits)
• So 4A = 0000100 1000001 — 4 as the count, 65 (A) as the character

Inference: 9618 asks for RLE in hexadecimal pairs and in prose. The binary form has never appeared in a question in either series, though SME teaches it and it is the only version that shows why RLE actually saves bits.

Question

A bitmap image has a bit depth of 1 bit. Explain how run-length encoding compresses it, and state the kind of saving that can be achieved.

Answer — 5 points for 3 marks

• The image is read as a stream of bits, and RLE creates frequency/data pairs
• … e.g. 30 11 20 11 20 11 50 11 … — three 0s (white) then one 1 (black), and so on
• Pairs can carry over onto the next line — the end of one row and the start of the next count as one run
• SME’s worked image drops from 195 bits to 38 bits, a saving of about 80%
• For images with more than two colours, the RGB value of each colour is stored with the count
• … e.g. 10 0 0 0 = ten black pixels, 5 255 0 0 = five red, 3 0 255 0 = three green

The carry-over across rows and the RGB form are both SME-only; 9618 marks RLE description questions at 2–3.