This module covers how a computer stores pictures and sound as binary data. You will calculate the size of that data, see how compression shrinks it, and convert between units such as bytes and KiB. These are some of the most reliable calculation marks in Computer Science, and they also test whether you can state assumptions clearly.
Check the current Cambridge IGCSE Computer Science 0478 syllabus page for the exact wording and the exam-year changes that apply to you. The methods here stay the same. Our Computer Science learning guide shows where this module sits among the others.
What should you already know?
You should be comfortable with bits and bytes and with binary place values. If converting between binary and denary still feels slow, revise binary and denary conversion and bit depth first, because every size calculation starts with how many bits one item needs.
An orienting example
A tiny image is 4 pixels wide and 4 pixels high. Each pixel uses 2 bits. How many bytes does the uncompressed image need?
Step 1, pixels: 4 × 4 = 16 pixels.
Step 2, bits: 16 × 2 = 32 bits.
Step 3, bytes: 32 ÷ 8 = 4 bytes.
Check: 2 bits give 4 possible colours (00, 01, 10, 11). A run-length compressed version of a plain background would be much smaller, which is the next idea in the module.
That one question used the image size method, the byte conversion and a link to bit depth. Sound works the same way, with sample rate, bit depth, length and channels in place of width and height.
In which order should you study the lessons?
- Calculate an uncompressed image size: pixels × bit depth, and why you must state your assumptions.
- Calculate a sampled audio size: sample rate × bit depth × time × channels.
- Distinguish lossless and lossy compression: what is kept, what is lost, and one working algorithm.
- Explain a quality-storage trade-off: linking a change to size and to quality in a sentence the examiner can credit.
- Convert storage units using the stated convention: the step that turns a huge number of bits into a readable answer.
Then work through the mixed practice set. The pseudocode trace trainer helps you step through the run-length example, and the Python reasoning sandbox lets you test a size formula as code.
Which traps catch most students here?
- Using the number of colours as the bit depth. 256 colours means 8 bits, not 256 bits.
- Forgetting to divide by 8 when the question asks for bytes.
- Leaving time in minutes when the sample rate is per second.
- Mixing 1000 and 1024 when converting. Use the convention the question or syllabus gives you.
- Saying “better quality” without a reason in a trade-off answer.
Each lesson shows one of these slips in full and then corrects it.
How should you use the practice set?
Attempt each question on paper first and write every step. Method matters because marks are usually given for the working as well as the final value. Open the answer only after your attempt, then read the routing notes at the end and return to the lesson it names.
Fix the lesson, then retry a similar question a few days later. If the same error keeps returning, our online one-to-one Computer Science tuition is built to find the habit behind it.