A Level Computer Science: Information Representation (Cambridge 9618)

This guide covers Topic 1 Information representation, an AS Level topic for Cambridge International AS & A Level Computer Science 9618, 2026 series, sat by both AS-only candidates and those continuing to the full A Level.

Where this fits in 9618

Information representation is the first topic candidates meet, and it establishes how all data — numbers, text, images, sound — is ultimately stored and manipulated as binary inside a computer system, a fact that remains true regardless of how abstracted the programming language or application layer a later topic discusses appears to be. Later topics on data structures, databases and networking all take for granted that candidates already understand how the underlying data is represented and how its size can be calculated and reduced. The syllabus is staged rather than tiered: AS Level candidates study sections 1-12, while the full A Level adds sections 13-20 — this topic sits within the AS-level core that every 9618 candidate covers.

Syllabus coverage

CAMBRIDGE AS & A LEVEL COMPUTER SCIENCE 9618 — TOPIC 1 INFORMATION REPRESENTATION

• 1.1 Data Representation — how numbers, text and other data types are represented in binary inside a computer system, including number base conversions between denary, binary and hexadecimal
• 1.2 Multimedia – Graphics, Sound — how images and sound are represented digitally, including the effect of resolution, colour depth, sample rate and sample resolution on file size and quality
• 1.3 Compression — why data compression is needed, and the difference between lossy and lossless compression methods, including named example techniques for each

How to approach it

Number representation and base conversion (1.1) are graded almost entirely on procedural accuracy, so timed practice converting between denary, binary and hexadecimal — including negative numbers where the syllabus requires it — closes most of the gap here, and a small number of careless arithmetic slips is a far more common cause of lost marks on this sub-topic than any genuine conceptual misunderstanding. For 1.2, practise calculating file sizes from given parameters (resolution × colour depth for images; sample rate × sample resolution × duration for sound) and be ready to explain, in words, how increasing any one of those parameters trades higher quality for larger file size — this explanatory skill is tested as often as the calculation itself. For 1.3, know a specific, real compression method for both lossy (such as reducing colour depth or sample rate) and lossless (such as run-length encoding) compression, since “describe how a file could be compressed” questions expect a genuine mechanism, not just the general concept that “compression makes files smaller.” Being able to state the trade-off each method involves — lossy compression permanently discards data in exchange for a smaller file, lossless compression allows exact reconstruction but achieves a smaller compression ratio — is often the specific distinction a mark is awarded for. Since this topic recurs throughout the practical programming components of the course whenever data storage or file handling comes up, treat fluency here as an investment that pays off well beyond Topic 1’s own exam questions — file-handling and data-structure topics later in the AS core (sections 1-12) both assume this level of comfort with how data is actually stored in memory.

Number base conversion — the core techniques

Converting denary to binary uses repeated division by 2 (reading remainders bottom to top), or subtracting the largest available power of 2 repeatedly. Converting binary to denary sums the place values of each set bit. Binary-to-hexadecimal conversion splits the binary number into groups of four bits (nibbles) from the right, converting each nibble to its single hex digit — this only works cleanly because 16 = 2⁴, which is exactly why hexadecimal is used as a compact way to represent binary in the first place. Two’s complement (used for negative numbers) inverts every bit of the positive binary representation and adds 1 — practise this alongside ordinary conversion, since exam questions frequently test both positive and negative numbers within the same question.

Calculating file sizes — the two core formulas

Image file size = image resolution (width × height, in pixels) × colour depth (bits per pixel). Increasing either the resolution or the colour depth increases quality but also increases file size proportionally — a question that asks you to calculate the effect of doubling resolution should show the file size roughly quadrupling (since both width and height double), not merely doubling.

Sound file size = sample rate (samples per second) × sample resolution (bits per sample) × duration (seconds). A higher sample rate captures the waveform more accurately (closer to the original analogue sound) and a higher sample resolution captures more possible amplitude values per sample — both trade higher audio quality for a larger file.

Lossy versus lossless compression

Lossless compression (such as run-length encoding, which replaces repeated sequences of the same value with a shorter code representing the value and its repeat count) allows the original file to be reconstructed exactly. Lossy compression (such as reducing colour depth in an image, or reducing sample rate in audio) permanently discards some information to achieve a smaller file, trading a small, often imperceptible loss in quality for a significantly smaller file size. Knowing which category a given real-world format or technique falls into — and being able to justify why — is tested as directly as the underlying calculations.

Official syllabus

Cambridge International AS & A Level Computer Science 9618 syllabus for 2026 — cambridgeinternational.org.