Bitmap Images (Cambridge (CIE) A Level Computer Science) : Revision Note

Robert Hampton

Written by: Robert Hampton

Reviewed by: James Woodhouse

Updated on

Bitmap encoding

What is a bitmap?

  • A bitmap image is made up of squares called pixels

  • A pixel is the smallest element of a bitmap image

  • Each pixel is stored as a binary code

  • Binary codes are unique to the colour in each pixel

  • A typical example of a bitmap image is a photograph

Blue rectangular electronic sensor with grid openings and four metal pins on the bottom for connectivity, casting a shadow on a white surface.
Humidity sensor bitmap image
  • The more colours and more detail in the image, the higher the quality of the image and the more binary that needs to be stored

Image vs screen resolution

  • Image resolution is the total amount of pixels that make up a bitmap image

  • The image resolution is calculated by multiplying the height and width of the image (in pixels)

  • In general, the higher the resolution the more detail in the image (higher quality)

  • Screen resolution refers to the total amount of pixels horizontally in a display, such as:

    • Computer monitors - 1440p means 1440 pixels horizontally compared to 4K which is 3840 pixels (roughly 4 thousand)

    • TVs - HD (high definition) televisions have a screen resolution of 1080p, 1080 pixels horizontally compared to newer 4K televisions with 3840 pixels

    • YouTube - The quality button allows a user to change the video playback resolution from 144p (144 pixels horizontally) up to 4K

  • Another consideration of screen resolution is the physical size of the of the display

  • The number of pixels per square inch (PPI) is known as pixel density

  • Pixel density can mean images to need to be scaled up or down to fit, sometimes causing loss in quality

Case Study

  • A consumer purchases a new 65" 4k television

  • The screen resolution is 3840 x 2160

  • To calculate the pixel density of the screen we add together the squares of the resolution

    • (38402 + 21602) = (14 745 600 + 4 665 600) = 19 411 200

    • Find the square root (square root of 19 space 411 space 200 end root = 4405.814)

    • Divide by the screen size (4405.814 / 65 = 68)

  • The television has a pixel density of 68 pixels per inch (PPI)

  • Watching 4K content from a normal viewing distance means the image will appear crisp and sharp

  • However, if you sit too close, you may start to see:

    • The pixel grid

    • A loss of fine detail

  • Modern smartphones have very high screen resolutions in much smaller screens

  • This gives them a much higher PPI, often over 300 PPI

  • This means they can be viewed up close without losing quality or seeing pixelation

Colour/bit depth

  • Colour depth is the number of bits stored per pixel in a bitmap image

  • The colour depth is dependent on the number of colours needed in the image

  • In general, the higher the colour depth the more detail in the image (higher quality)

  • In a black & white image the colour depth would be 1, meaning 1 bit is enough to create a unique binary code for each colour in the image (1=white, 0=black)

Example 1 bit black and white image
  • In an image with a colour depth of 2, you would have 00, 01, 10 & 11 available binary codes, so 4 colours

Example 2 bit Mario image
  • As colour depth increases, so does the amount of colours available in an image

  • Colour depth can also refer to the number of colours that can be represented in an image

  • It is calculated using the formula:

    • Colour depth = 2n (where n = number of bits)

Colour/bit depth

Number of colours

Example

1 bit

21 = 1

B&W

2 bit

22 = 4

Icons/logos

4 bit

24 = 16

Early computer graphics

8 bit

28 = 256

GIFs, retro games

24 bit (True colour)

224 = 16 777 216

High-quality images

Calculating the size of a bitmap file

How do you calculate the size of a bitmap image?

  • Estimating the size of a bitmap image can be carried out with the following formula:

    • Resolution x colour/bit depth

Example

Image Files

(Resolution) x (Colour Depth)

Size of bitmap image = 

 

 

Resolution

250,000

Resolution = width x height

Colour depth

24 bits

24 bits = 3 bytes

250,000 x 24

=

(bit to bytes) /8

(bytes to KiB) /1024

or

(bytes to KB) /1000

6,000,000 bits

750,000 bytes

732 KiB

or

750 KB

250000 x 3

=

(bytes to KiB) /1024

or

(bytes to KB) /1000

750,000 bytes

732 KiB

or

750 KB

  • When bitmap images are saved, a file header is created

  • This contains:

    • File type (.bmp or .jpg)

    • File size

    • Image resolution

    • Colour depth

    • Any type of compression if used

Worked Example

The following section of a bitmap image is 10 pixels wide and 5 pixels high. In this example, each colour is represented by a letter, e.g. O is orange.

The complete image can have up to 256 colours

A grid of letters with ten columns and six rows containing letters B, Y, R, M, P, K, T, G, and O in various sequences and positions.

a) Identify the smallest number of bits that can be used to represent each colour in the complete bitmap image. [1]

b) Calculate an estimate for the file size of the section of the bitmap image shown, giving your answer in bytes. Use your answer from part a and show your working. [2]

Answer

  • 8 [1 mark]

  • 10 x 5 = 50(pixels), 50 x 8(bits) = 400 // 400 ÷ 8(bits) = 50 [1 mark]

  • 50 (bytes) [1 mark]

Image quality & file size

What is the relationship between image quality and file size?

  • As the resolution and/or colour depth increases, the bigger the size of the file becomes on secondary storage

  • The higher the resolution, the more pixels are in the image, the more bits are stored

  • The higher the colour depth, the more bits per pixel are stored

  • Striking a balance between quality and file size is always a consideration

  • Compression can be used on high quality images to help reduce the file size

Seesaw to demonstrate the relationship between quality and file size of an image

You've read 0 of your 5 free revision notes this week

Unlock more, it's free!

Join the 100,000+ Students that ❤️ Save My Exams

the (exam) results speak for themselves:

Did this page help you?

Robert Hampton

Author: Robert Hampton

Expertise: Computer Science Content Creator

Rob has over 16 years' experience teaching Computer Science and ICT at KS3 & GCSE levels. Rob has demonstrated strong leadership as Head of Department since 2012 and previously supported teacher development as a Specialist Leader of Education, empowering departments to excel in Computer Science. Beyond his tech expertise, Robert embraces the virtual world as an avid gamer, conquering digital battlefields when he's not coding.

James Woodhouse

Reviewer: James Woodhouse

Expertise: Computer Science Lead

James graduated from the University of Sunderland with a degree in ICT and Computing education. He has over 14 years of experience both teaching and leading in Computer Science, specialising in teaching GCSE and A-level. James has held various leadership roles, including Head of Computer Science and coordinator positions for Key Stage 3 and Key Stage 4. James has a keen interest in networking security and technologies aimed at preventing security breaches.