Storage units, text, sound and images
Convert binary storage units and explain how character sets, sampled sound and bitmap images encode information.
Mapped to the Cambridge IGCSE Computer Science 0265 syllabus for exams in 2029. These original explanations and self-checks are independent study support, not Cambridge papers, official marks or grades. Practical programming is taught with worked code and trace tasks; an executable Python workspace and timed digital-exam simulation are not yet available.
Before you begin
- Use the 2029 syllabus for this course. Cambridge specifies Python 3.10 or higher for programming and digital exams for both components. The syllabus currently identifies this route for 2029 only; confirm later-year availability with Cambridge and your school.
Work on paper. Try each question before revealing help, and explain why your method works.
Quick readiness check
Before storage units, text, sound and images, which statement is the safest starting point?
What you will learn
- Convert bits, nibbles, bytes and binary storage units
- Compare ASCII and Unicode
- Explain how sound and bitmap properties affect data and quality
Measure storage consistently
A bit is one binary digit; a nibble is four bits and a byte is eight bits. The syllabus uses binary units: 1 KiB = 1024 bytes, then each larger unit (MiB, GiB, TiB) is 1024 of the preceding unit. Keep the unit labels visible while converting.
Text is stored by assigning numeric codes to characters. ASCII uses a small character set and is compact for its supported characters, while Unicode represents a much wider range of writing systems and symbols, usually with more storage overhead depending on encoding.
Sound and pictures sample the world
A computer samples analogue sound at regular intervals and stores each sample using a finite resolution. A higher sample rate captures more changes per second; a higher sample resolution allows more amplitude levels. Both can improve fidelity and increase file size.
A bitmap is a grid of pixels. Resolution is the number of pixels; colour depth is the number of bits used per pixel. Increasing either can represent more visual detail or colours, but increases storage. Raw bitmap size is approximately width × height × colour depth, before headers and compression.
Pause and explain
Which habit best checks your understanding of storage units, text, sound and images?
Worked example
Estimate the raw data in a 160 × 100 bitmap with a colour depth of 8 bits.
Show the worked solution
- Pixel count: 160 × 100 = 16 000.
- Data bits: 16 000 × 8 = 128 000 bits.
- Convert bits to bytes by dividing by 8.
Answer 16 000 bytes, before headers or compression.
Try a topic test
Choose Easy, Normal or Difficult. Save your answers and open worked review after finishing.
Test this topic →From guided practice to a new situation
Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.
Convert 3 KiB to bytes and then to bits.
Give me a hint
Use 1024 bytes per KiB and 8 bits per byte.
Compare my reasoning
- 3 × 1024 = 3072 bytes.
- 3072 × 8 = 24 576 bits.
- Keep both units in the final statement.
3072 bytes = 24 576 bits
Look for these in your work
- Binary unit used
- Byte-to-bit conversion correct
- Units shown
A mono recording lasts 5 seconds at 8000 samples per second and 8 bits per sample. Find its uncompressed size in bytes.
Give me a hint
Multiply duration, sample rate and resolution, then convert bits to bytes.
Compare my reasoning
- Number of samples: 5 × 8000 = 40 000.
- Bits: 40 000 × 8 = 320 000.
- Bytes: 320 000 ÷ 8 = 40 000.
40 000 bytes, excluding headers.
Look for these in your work
- Samples calculated
- Bit depth applied
- Bytes calculated
A designer doubles both bitmap dimensions but halves the colour depth. Explain the change in raw data size.
Give me a hint
Compare the change in pixel count with the change in bits per pixel.
Compare my reasoning
- Doubling width and height makes four times as many pixels.
- Halving colour depth uses half as many bits per pixel.
- The combined factor is 4 × 1/2 = 2.
Raw data size doubles, before headers or compression.
Look for these in your work
- Both dimensions considered
- Depth change considered
- Combined factor justified
Common mistakes
- Using 1000 instead of 1024 for KiB.
- Confusing sample rate with sample resolution.
- Treating image resolution and colour depth as the same property.
What can you explain now?
If colour depth doubles but resolution stays fixed, what happens to the raw bitmap size?
Compare with the explanation
It doubles
Each pixel now needs twice as many bits.
After trying it yourself, choose your next review. This is your self-assessment.
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Make it stick
How do a character set, sound sampling and bitmap properties turn information into bits?
If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.