Representing images

What a pixel is

A pixel is a single point in an image. The word is short for picture element — the specification states this explicitly, so use the full term if asked.

A screen displays an image by lighting up a grid of pixels, each showing one colour. Because the pixels are very small and packed closely together, the eye blends them into a continuous picture.

How a bitmap stores an image

A bitmap image is made up of a grid of pixels, where the colour of each pixel is stored as a binary value.

That sentence is the mark-scheme answer to "describe how a bitmap represents an image using pixels and colour depth".

Why images look blocky when enlarged

Because a bitmap stores a fixed grid of pixels, enlarging it does not add detail — the existing pixels simply become bigger squares. This is pixelation, and it is a direct consequence of the image being stored as a fixed number of discrete points.

The two properties that define a bitmap

Property	What it means	Measured in
Image size	How many pixels the image contains	pixels, written width × height
Colour depth	How many bits store each pixel's colour	bits per pixel

Together these determine both the quality and the file size — and they are the only two values needed for the calculation.

AQA tip. Use the specification's own phrasing: a pixel is "a single point in an image" and the term is short for "picture element". Both details are creditworthy in a "what is a pixel?" question.

Colour depth is the number of bits used to represent each pixel.

The relationship to the number of colours is the same counting rule as everywhere else in this unit:

$\text{number of colours} = 2^D$

Colour depth	Colours available	Typical use
1 bit	2	Black and white
2 bits	4	Very simple graphics
4 bits	16	Early computer graphics
8 bits	256	Simple photos, GIF images
16 bits	65,536	Good-quality images
24 bits	16,777,216	True colour photographs

Why 24-bit is called "true colour"

24 bits are split into three groups of 8 — one byte each for red, green and blue. Each colour component therefore has 256 possible levels (0–255), and combining them gives 256 × 256 × 256 ≈ 16.7 million colours, which is more than the human eye can distinguish.

This is exactly the hexadecimal colour code from 3.3.1: #FF8000 is 255 red, 128 green, 0 blue — two hex digits per component, one byte each, 24 bits in total.

Each extra bit doubles everything

Adding one bit to the colour depth:

• Doubles the number of available colours
• Increases the file size proportionally (a 2-bit image is twice the size of a 1-bit image of the same dimensions)

$1 \text{ bit} \to 2 \text{ colours} \quad 2 \text{ bits} \to 4 \quad 3 \text{ bits} \to 8 \quad 4 \text{ bits} \to 16$

The trade-off

Higher colour depth gives better image quality — smoother gradients, more realistic photographs, no visible colour banding — but a larger file.

Reducing colour depth from 24 bits to 8 bits cuts the file size to one third, but limits the image to 256 colours, which can produce visible banding in areas of gradual shading such as skies.

AQA tip. If asked how many colours a given depth provides, show the power: "8 bits gives 2⁸ = 256 colours." And if asked for the depth needed for a stated number of colours, work backwards: 16 colours needs 2ᴰ = 16, so D = 4 bits.

The specification gives both forms of the formula verbatim:

$\text{Size (bits)} = W \times H \times D$ $\text{Size (bytes)} = \frac{W \times H \times D}{8}$

where W = image width, H = image height, D = colour depth in bits.

The method, every time

1. Identify W, H and D from the question.
2. Multiply all three → size in bits.
3. Divide by 8 → size in bytes.
4. Divide by 1,000 as many times as needed → kB, MB (3.3.3).
5. Sense-check the answer against real-world scale.

Worked example

An image is 800 pixels wide by 600 pixels high with a colour depth of 24 bits. Calculate its file size in megabytes.

Step 1 — bits: $800 \times 600 \times 24 = 11{,}520{,}000 \text{ bits}$

Step 2 — bytes: $11{,}520{,}000 \div 8 = 1{,}440{,}000 \text{ bytes}$

Step 3 — kilobytes: $1{,}440{,}000 \div 1{,}000 = 1{,}440 \text{ kB}$

Step 4 — megabytes: $1{,}440 \div 1{,}000 = 1.44 \text{ MB}$

Sense-check: a 0.5-megapixel photograph at true colour being around 1.4 MB is entirely plausible ✓

Working backwards

Questions sometimes give you the file size and ask for a missing value. Rearrange the formula:

$D = \frac{\text{Size (bits)}}{W \times H}$

A 100 × 50 image occupies 5,000 bytes. Find the colour depth.

• Convert to bits: 5,000 × 8 = 40,000 bits
• Number of pixels: 100 × 50 = 5,000
• Colour depth: 40,000 ÷ 5,000 = 8 bits

What is NOT included

"Students only need to use colour depth and number of pixels within their calculations."

So ignore metadata — real image files also store a header with dimensions, format and colour information, but AQA excludes it. If a question mentions metadata, note that it makes the actual file slightly larger, but do not attempt to include a figure.

AQA tip. Always show every step. Mark schemes award method marks for the multiplication and separately for the ÷8, so a wrong final answer with correct working still scores. Label your units at each stage — "bits", "bytes", "kB" — because losing track of which unit you are in is the most common error.

The specification asks you to "describe using examples how the number of pixels and colour depth can affect the file size" — so an answer needs numbers, not just assertions.

Effect of increasing the number of pixels

Take a 24-bit image and change only its dimensions:

Dimensions	Pixels	Size (bits)	Size (bytes)
100 × 100	10,000	240,000	30,000
200 × 200	40,000	960,000	120,000
400 × 400	160,000	3,840,000	480,000

Doubling both width and height gives four times as many pixels, so the file becomes four times larger. This catches people out — doubling the dimensions quadruples the area.

Effect of increasing colour depth

Take a 200 × 200 image and change only the depth:

Colour depth	Colours	Size (bits)	Size (bytes)
1 bit	2	40,000	5,000
8 bits	256	320,000	40,000
24 bits	16.7 million	960,000	120,000

Colour depth scales the file size directly — 24-bit is exactly 24 times the size of 1-bit for the same dimensions.

The quality trade-off

Change	Quality effect	Size effect
More pixels	More detail; can be enlarged further before pixelating	Larger file
Fewer pixels	Loss of detail; pixelates sooner	Smaller file
Greater colour depth	More colours; smoother gradients, more realistic	Larger file
Lower colour depth	Fewer colours; visible banding in gradients	Smaller file

Choosing appropriately

• A photograph needs high resolution and 24-bit depth for realism.
• A simple icon or logo with flat colours needs far less of both — using 24-bit for a two-colour logo wastes 23 bits per pixel.
• A web image must balance quality against download time.

AQA tip. When asked to describe the effect, give a worked comparison: "Increasing the colour depth from 8 bits to 24 bits triples the file size — a 200 × 200 image grows from 40,000 bytes to 120,000 bytes — but allows 16.7 million colours instead of 256." Numbers turn a 2-mark answer into a 4-mark one.

The specification requires both directions, and this is the part students most often skip in revision.

Binary → bitmap image (draw it)

Given a binary pattern representing a bitmap, draw the resulting image as a series of pixels.

Method:

1. Work out the grid dimensions from the question.
2. Split the bit pattern into rows of the correct width.
3. Colour each pixel according to its bit value, working left to right, top to bottom.

Draw the 4 × 4 image represented by 0110 1001 1001 0110, where 0 = white and 1 = black.

Split into four rows of four:

0 1 1 0     □ ■ ■ □
1 0 0 1     ■ □ □ ■
1 0 0 1  →  ■ □ □ ■
0 1 1 0     □ ■ ■ □

The result is a diamond outline.

Bitmap image → binary (write the bits)

Given a simple bitmap, write down the bit pattern that represents it.

Method: read the grid left to right, top to bottom — exactly the same order — and write 0 or 1 for each pixel.

For the image above, reading row by row gives 0110 1001 1001 0110 ✓

With more than one bit per pixel

If the colour depth is 2 bits, each pixel needs two bits:

00 = white   01 = light grey   10 = dark grey   11 = black

A 2 × 2 image with the pattern 00 11 10 01 gives:

white	black
dark grey	light grey

Count carefully: with a depth of D bits, a row of W pixels uses W × D bits.

The checks

1. Count the bits. The total must equal W × H × D. A 4 × 4 image at 1 bit needs exactly 16 bits.
2. Check the row length. Each row must contain exactly W pixels.
3. Confirm the reading order — always left to right, top to bottom.

AQA tip. Before drawing, count the bits and divide by the width to confirm the number of rows. If the arithmetic does not come out exactly, you have misread the depth or the dimensions.