Representing Images (OCR GCSE Computer Science)

Vectors & Bitmaps

• Computers represent all data in binary, including images that are seen on a screen, TV or other output device 

• Images can be stored in binary as Bitmap or Vector

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

• 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

What is a vector?

• A vector image is created from mathematical equations and points

• Only the mathematics used to create the image are stored

• For example, to create a circle the data stored would be:

  • Centre point (x, y coordinates)

  • Radius

• Typical examples of vector images are logos and clipart

• Vector images are infinitely scalable

• Ideal for situations where the same image will be made bigger and smaller and a loss of quality is unacceptable. For example, the same logo used on both a pencil and a billboard

Resolution & Colour Depth

What is resolution?

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

• The 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)

• Resolution can also refer 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) channels have a resolution of 1080p, 1080 pixels horizontally compared to newer UHD (ultra high definition) channels with 3840 pixels (4K)

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

What is colour 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)

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

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

• The amount of colours can be calculated as 2ⁿ (n = colour depth) 

What is the impact of resolution and colour depth?

• 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

Metadata

What is metadata?

• Metadata is data about data

• Metadata is additional information stored with the image, although not required to display the image it provides context and information

• Examples of metadata that can be stored are:

  • Author - Who created the image?

  • Date/Time - When and what time was the image created/taken?

  • Location - Where was the image taken?

  • Width & height of the image (resolution)

  • Colour depth