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Candidates should be able to:
- explain the representation of an image as a series of pixels represented in binary
- explain the need for metadata to be included in the file such as height, width and colour depth
- discuss the effect of colour depth and resolution on the size of an image file.
How can an image be represented in binary?
Bitmap images are made up of individual pixels. The colour of each pixel is represented as a binary number so the whole image is therefore stored as a series of binary numbers.
In the example on the right the animation zooms into the small image to show the individual pixels. In this example only 1 bit is used to represent each pixel so the binary code for each pixel is 1 or 0, giving 2 possible colours. This image therefore has a colour depth of 1 bit.
If more memory bits are used to represent each pixel then more combinations of binary numbers are possible so more colours are possible in the image.
Vector images store information as mathematical instructions rather than as individual pixels. For example, to save a vector image of a circle the software only needs to store:
- the coordinates of the circle centre
- the radius
- the line thickness
- the line style and colour
- the fill type and colour
- the image scale.
This information would be stored using binary codes for the instructions. The result is that the file size of a vector image would be considerably smaller than the equivalent bitmap image which would need to store the colour information for every pixel.
Although vector images can have graduated fills these are mathematically defined and cannot represent the level of detail needed for photographic images.
As well as the smaller file sizes, vector images have the advantage that they can be scaled to any size without the loss of detail that would occur with a bitmap image. Also, if a vector image was scaled-up and then saved it would have the same file size as the original.
Why does metadata need to be included in an image file?
Metadata is needed in a bitmap image file because the software that displays an image needs to know:
- The height and width of the image – so each line of the image starts in the correct place.
- The resolution – so the image displays at the correct size.
- The colour depth – so the correct number of bits are used to represent the colour of each pixel.
|An image with the CORRECT colour depth metadata – 1 bit colour depth||The same data but the WRONG colour depth metadata – 2 bit colour depth|
|An image with the CORRECT size metadata – 16 x 24 pixels||The same data but with the WRONG size
metadata – 24 x 16 pixels
NOTE: An image file may also contain metadata describing the type and amount of compression in the image, the software used to create the file and, if it is a digital photograph, the date and time the image was taken and the camera/lens settings.
What is colour depth and how does it effect the size of an image file?
depth in bits
binary code(s) used to
store the colour information
about each pixel
|2||4||00, 01, 10, 11|
|3||8||000, 001, 111 etc.|
|4||14||0000, 0001, 1110 etc.|
|8||256||10010101, 11101101 etc.|
Colour depth describes the number of bits of memory that are used to store the colour information about each pixel in a bitmap image.
With 1 bit colour depth the number of bits used to store the information about each pixel is 1. This allows 2 colours, represented by 0 or 1.
With 3 bit colour depth the number of bits used to store the information about each pixel is 3. This allows 8 colours, represented by the binary codes 000, 001, 010, 011, 100, 101, 110 or 111.
SUMMARY: The greater the colour depth of a bitmap image, the greater the file size because more memory is used to store the colour data about each pixel.
Colour mapping, palette tables and Direct colour
Colour mapping – With low colour depths (up to 8 bit) it is practical to map every colour to a binary code because there are not too many colours involved. This is called colour mapping and an example might be the binary code 110 representing yellow.
Palette tables – The GIF file format uses 8 bits for each pixel so each pixel can display 256 different colours. However, every GIF image can have a different set of 256 colours because they are stored as part of the file in a palette table. This table stores each of the 256 colours using 24 bit direct colour giving 16,777,216 possible colours. Each of the 256 colours in the palette is indexed using 8 bits so it is the 8 bit index value that is actually stored for each pixel in a GIF file.
Direct colour – As the colour depth increases, colour mapping and palettes become impractical due to the number of possible colours that the image can display. In higher colour depths (8 bit and above) a system called direct colour is used instead. In this the bits allocated to each pixel separately encode the relative proportions of Red, Green and Blue to specify what is called an RGB colour. For example, if 24 bit direct colour was used then the code for a yellow pixel (maximum red and green and zero blue) would be;
- 255, 255, 0 in denary
- 111111111111111100000000 in binary (note the use of 24 bits)
- FFFF00 in hexadecimal
EXPANSION TOPIC – Colour mapping and Direct colour
What is resolution and how does it effect the size of an image file?
Resolution is a measure of how much detail there is in an image.
In a bitmap image the resolution depends on the pixel density, the number of pixels per unit (not the total number of pixels in the image). The resolution depth of a screen image is usually measured in pixels per inch (PPI). The resolution depth of a printed image is usually measured in dots per inch (DPI).
A high resolution image can be magnified and still stay sharp. A low resolution image will appear pixelated (the individual pixels will be clearly visible) if it is magnified to the same size as a high resolution image.
SUMMARY: A bitmap image with a high resolution will have a greater the file size than the equivalent low resolution image because more memory is used to store the colour data of the extra pixels.
NOTE: Because vector images are only stored as a set of mathematical instructions, halving the size of a vector image would effectively make no difference to the file size. However, halving the dimensions of a bitmap image would effectively make the file size ¼ of the original.