How is text represented in computer systems for the Cambridge IGCSE Computer Science curriculum?

In the Cambridge IGCSE Computer Science curriculum, text is represented using character encoding schemes such as ASCII and Unicode. ASCII uses 7 or 8 bits to represent characters, allowing for 128 or 256 different symbols, respectively. Unicode extends this to accommodate a vast range of characters from different languages, using up to 32 bits, which allows for over a million unique symbols.

What is the process of converting sound into a digital format in data representation?

Converting sound into a digital format involves sampling and quantization. In the Cambridge IGCSE Computer Science curriculum, students learn that sound waves are sampled at regular intervals (sampling rate) and each sample is then assigned a numerical value (quantization). The quality of the digital sound depends on the sampling rate and bit depth, with higher values providing better quality.

How are images stored and represented in computer systems according to the Cambridge IGCSE syllabus?

Images are stored and represented in computer systems as a grid of pixels, where each pixel has a specific color value. In the Cambridge IGCSE syllabus, students learn about bitmap and vector graphics. Bitmap images store color information for each pixel, while vector graphics use mathematical formulas to represent shapes and colors, allowing for scalability without loss of quality.

What is the difference between lossy and lossless compression in the context of images and sounds?

Lossy compression reduces file size by removing some data, which may result in a loss of quality. It's commonly used in JPEG images and MP3 audio files. Lossless compression, on the other hand, reduces file size without losing any data, preserving the original quality. Formats like PNG for images and FLAC for audio use lossless compression. The Cambridge IGCSE Computer Science curriculum covers these concepts to help students understand data efficiency and quality trade-offs.

Why is binary important in representing text, sounds, and images in computer systems?

Binary is crucial because it is the fundamental language of computers, which operate using two states: on and off, represented by 1s and 0s. In the Cambridge IGCSE Computer Science curriculum, students learn that text, sounds, and images are all ultimately converted into binary code for processing and storage. This uniformity allows computers to handle diverse types of data efficiently and accurately.

Why is "Forgetting to convert bits to bytes" a common mistake in Text, Sounds and Images?

Why it happens: Calculations naturally end in bits. How to avoid it: If the question asks for BYTES (or KB/MB), divide by 8. Show this step explicitly. Source: 0478 Examiner Reports 2022-2024

Why is "Forgetting to × 2 for stereo sound" a common mistake in Text, Sounds and Images?

Why it happens: Stereo seems implicit in modern audio. How to avoid it: If 'stereo' or 'two channels' is mentioned, multiply the per-channel size by 2.

Why is "Saying 'ASCII represents text'" a common mistake in Text, Sounds and Images?

Why it happens: Vague. How to avoid it: Be precise: ASCII assigns a unique 7-bit binary CODE to each character. Each character → a specific number → a binary string.

Data RepresentationCambridge IGCSE Computer Science (0478)

Text, Sounds and Images

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Text, Sound and Images Study Notes — Cambridge IGCSE Computer Science 0478 (2026-2028 syllabus)

How computers represent the three main media types: text (ASCII / Unicode), sound (samples), and images (pixels). The file-size formulas are guaranteed exam content.

What you’ll learn

Mapped to the Cambridge IGCSE 0478 syllabus (2026-2028).

1.1.6 — Describe how characters are represented (ASCII, Unicode).
1.1.7 — Describe how images are represented (resolution, colour depth).
1.1.8 — Describe how sound is represented (sample rate, bit depth).
1.1.9 — Calculate file sizes for images and sound.

Representing text — ASCII and Unicode

Each character → a binary code. ASCII for English; Unicode for everything.

Computers don't 'understand' text directly. Each character (letter, digit, symbol) is mapped to a numerical CODE, then stored as binary.

ASCII (American Standard Code for Information Interchange). Uses 7 bits per character → 128 codes. Examples:

'A' = 65 = 1000001
'a' = 97 = 1100001
'0' = 48 = 0110000
' ' (space) = 32 = 0100000

ASCII covers English uppercase + lowercase, digits, and basic punctuation. Extended ASCII uses 8 bits → 256 codes, adding accented characters and symbols.

Limitation. ASCII is English-centric. It can't represent Chinese, Arabic, Hindi, emoji, or thousands of other characters.

Unicode. Modern standard supporting characters from every writing system. Uses up to 32 bits per character → over 1 million possible codes. The most common encoding is UTF-8, which uses 8-32 bits depending on the character (and is backwards-compatible with ASCII for the first 128 codes).

Cambridge tip. Mark scheme rewards (a) the precise definition of ASCII (7-bit, 128 chars), (b) at least one character→code example, and (c) why Unicode was developed (more characters, all scripts).

ASCII: 7-bit, 128 chars, English-centric.
Unicode: up to 32-bit, all scripts.
Each character has a unique numerical code → binary.
UTF-8 is the dominant Unicode encoding.

Representing images — pixels, resolution, colour depth

Image = grid of pixels. Each pixel = a colour. File size = width × height × bit depth.

A bitmap image is a grid of pixels. Each pixel stores a colour as a binary number.

Three key parameters:

Resolution = width × height in pixels. e.g., 1920 × 1080.
Colour depth (bit depth) = bits per pixel. More bits = more colours.
- 1-bit: 2 colours (black/white).
- 8-bit: 256 colours.
- 24-bit (RGB): 256 × 256 × 256 ≈ 16.7 million colours (one byte each for R, G, B).

File size formula:

$\text{Size (bits)} = \text{width} \times \text{height} \times \text{colour depth}$

To convert to bytes, divide by 8.

Worked example. 800 × 600 image, 24-bit colour:

Bits = 800 × 600 × 24 = 11,520,000.
Bytes = 11,520,000 ÷ 8 = 1,440,000 bytes ≈ 1.4 MB.

Trade-off. Higher resolution + higher colour depth = better image quality BUT larger file. Compression algorithms (JPEG, PNG) reduce file size by removing redundancy or perceptually-irrelevant detail.

Cambridge tip. Always state the formula, then substitute, then convert to bytes. Mark scheme awards method marks separately from the final answer.

Image = grid of pixels.
Resolution × colour depth → file size.
24-bit = 16.7M colours = 'true colour'.
Always ÷ 8 to convert bits → bytes.

See the full worked example for text, sounds and images →

Representing sound — sampling and quantisation

Sound is measured at intervals. Sample rate × bit depth × channels × duration = file size.

Sound is a continuous wave. Computers turn it into binary by sampling — measuring the wave's amplitude at regular intervals.

Three key parameters:

Sample rate = how many samples per second. Measured in Hz. CD quality = 44,100 Hz.
Bit depth (sample resolution) = bits per sample. CD quality = 16-bit.
Channels = mono (1) or stereo (2).

File size formula:

$\text{Size (bits)} = \text{sample rate} \times \text{bit depth} \times \text{channels} \times \text{duration (s)}$

To convert to bytes, divide by 8.

Worked example. 30-second stereo recording at 44,100 Hz, 16-bit:

Bits/s/channel = 44,100 × 16 = 705,600.
Bits/s stereo = 705,600 × 2 = 1,411,200.
Total bits = 1,411,200 × 30 = 42,336,000.
Bytes = 42,336,000 ÷ 8 = 5,292,000 bytes ≈ 5 MB.

Quality vs size trade-off.

Higher sample rate → captures higher-frequency detail (music sounds richer) but larger file.
Higher bit depth → finer amplitude resolution (less quantisation noise) but larger file.

Why 44,100 Hz? The Nyquist theorem says you need to sample at twice the highest frequency you want to capture. Human hearing tops out at ~22,000 Hz, so 44,100 Hz is just above double — the minimum that captures full audible range.

Cambridge tip. Always use the four-multiplier form: sample rate × bit depth × channels × duration. Forgetting any one is a common method-mark loss.

Sound = continuous wave → discrete samples.
Sample rate × bit depth × channels × duration.
Don't forget the channels (× 2 for stereo).
Always ÷ 8 for bytes.

See the full worked example for text, sounds and images →

How it’s examined

File-size calculations appear on most Paper 1 sittings (4-6 marks). ASCII vs Unicode is a near-guaranteed short-answer question. Examiner reports flag candidates who skip steps or forget to convert bits→bytes.

Sources: Cambridge IGCSE Computer Science 0478 syllabus (2026-2028); 0478 Examiner Reports 2022-2024; 0478/12 May/Jun 2024 question paper and mark scheme. Last reviewed 2026-05-09.

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Step-by-step worked examples — Text, Sounds and Images

Step-by-step solutions to past-paper-style questions on text, sounds and images, written exactly the way a tutor would explain them at the board.

1Calculate image file size (4 marks)
Extended• images, file size
Question
An image is 800 pixels wide × 600 pixels tall, with a colour depth of 24 bits per pixel. Calculate its file size in bytes. (4 marks)
Step-by-step solution
1. Step 1
  Total pixels. 800 × 600 = 480,000 pixels.
2. Step 2
  Total bits. 480,000 × 24 = 11,520,000 bits.
3. Step 3
  Convert to bytes. 11,520,000 ÷ 8 = 1,440,000 bytes.
4. Step 4
  Express in larger units (optional). 1,440,000 bytes ≈ 1.4 MB.
Answer
File size = 1,440,000 bytes (≈ 1.4 MB).
Examiner tip
Mark scheme: 1 mark for total pixels, 1 for multiplying by colour depth, 1 for ÷ 8 (bits→bytes), 1 for the final answer. Show every step.
2Calculate sound file size (5 marks)
Extended• Adapted from 0478/12 May/Jun 2024 Q3• sound, file size
Question
A sound recording is 30 seconds long, sampled at 44,100 Hz with a sample resolution of 16 bits, in stereo (2 channels). Calculate the file size in bytes. (5 marks)
Step-by-step solution
1. Step 1
  Bits per second per channel. 44,100 samples × 16 bits = 705,600 bits/s.
2. Step 2
  For stereo, multiply by 2 channels. 705,600 × 2 = 1,411,200 bits/s.
3. Step 3
  Total bits for 30 seconds. 1,411,200 × 30 = 42,336,000 bits.
4. Step 4
  Convert to bytes. 42,336,000 ÷ 8 = 5,292,000 bytes.
Answer
File size = 5,292,000 bytes (≈ 5 MB).
3ASCII character codes (3 marks)
Core• ASCII, text
Question
ASCII represents characters using 7 bits. Explain how ASCII works and why Unicode was developed. (3 marks)
Step-by-step solution
1. Step 1
  ASCII (1 mark). Each character is mapped to a unique 7-bit code (0-127). E.g., 'A' = 65, 'a' = 97, '0' = 48.
2. Step 2
  Limitation (1 mark). Only 128 characters total — enough for English letters, digits, basic punctuation, but not for non-Latin scripts (Chinese, Arabic, etc.) or many symbols.
3. Step 3
  Unicode (1 mark). Uses up to 32 bits per character → over 1 million possible codes. Covers every world script, emoji, and historical symbol. Standardised across modern systems (UTF-8 is the dominant encoding).
Answer
ASCII: 7-bit codes for 128 characters (English-centric). Unicode: up to 32-bit codes for millions of characters across all world scripts.

Key Formulae — Text, Sounds and Images

The formulae you need to memorise for text, sounds and images on the Cambridge IGCSE 0478 paper, with every variable defined in plain English and a note on when to use it.

Image file size (uncompressed)
$\text{Size (bits)} = \text{width} \times \text{height} \times \text{colour depth}$
$\text{width, height}$
in pixels
$\text{colour depth}$
bits per pixel
When to use
Calculating bitmap image file size.
Example
800 × 600 × 24 = 11,520,000 bits = 1,440,000 bytes ≈ 1.4 MB
Sound file size (uncompressed)
$\text{Size (bits)} = \text{sample rate} \times \text{bit depth} \times \text{channels} \times \text{duration}$
$\text{sample rate}$
Hz (samples per second)
$\text{bit depth}$
bits per sample
$\text{channels}$
1 mono, 2 stereo
$\text{duration}$
seconds
When to use
Calculating audio file size before compression.
Example
30 s, 44,100 Hz, 16-bit, stereo → 30 × 44,100 × 16 × 2 = 42,336,000 bits ≈ 5 MB

Key Definitions and Keywords — Text, Sounds and Images

Definitions to memorise and the exact keywords mark schemes credit for text, sounds and images answers — sharpened from recent examiner reports for the 2026 0478 sitting.

ASCII
Examiner keyword
American Standard Code for Information Interchange. 7-bit character encoding for 128 characters (English letters, digits, basic punctuation).
Unicode
Examiner keyword
Character encoding standard supporting characters from every world writing system. Up to 32 bits per character.
Pixel
Examiner keyword
Picture element — the smallest dot in a digital image. An image is a grid of pixels.
Resolution
Examiner keyword
Number of pixels in an image — width × height. Higher resolution = more detail = larger file size.
Colour depth (bit depth)
Examiner keyword
Number of bits used per pixel. 1-bit = 2 colours; 8-bit = 256 colours; 24-bit = ~16.7 million colours.
Sample rate
Examiner keyword
How many times per second sound is measured. Measured in Hz. CD quality = 44,100 Hz.
Sample resolution / bit depth
Examiner keyword
Bits per sample. Higher = better quality and larger files. CD quality = 16-bit.

Common Mistakes and Misconceptions — Text, Sounds and Images

The traps other students keep falling into on text, sounds and images questions — taken from recent Cambridge IGCSE 0478 examiner reports and mark schemes — and how to avoid them.

✕Forgetting to convert bits to bytes
0478 Examiner Reports 2022-2024
Why it happens
Calculations naturally end in bits.
How to avoid it
If the question asks for BYTES (or KB/MB), divide by 8. Show this step explicitly.
✕Forgetting to × 2 for stereo sound
Why it happens
Stereo seems implicit in modern audio.
How to avoid it
If 'stereo' or 'two channels' is mentioned, multiply the per-channel size by 2.
✕Saying 'ASCII represents text'
Why it happens
Vague.
How to avoid it
Be precise: ASCII assigns a unique 7-bit binary CODE to each character. Each character → a specific number → a binary string.

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