Graphics and compression

Smallest addressable area of an image

VDU screen: number of pixels per row*number of pixels per column An image: Number of pixels per inch/centimetre

Number of bits used to represent a colour of a single pixel

Image size(in bits) = bits per colour * number of pixels

Information about data stored in same file as image data Including: date, colour depth, location, etc

They are co-ordinates of objects making up pictures stored as mathematical data. Points described relative from origin. The objects are comprised of paths, can be scaled and group together.

Start and end point coordinates Length Thickness Colour

Lossless compression and lossy compression

Lossless compression: File can be uncompressed, and o.g quality of image can be restored Lossy compression: Some o.g image quality lost forever. So when decompress you cannot get it back

Storing at reduced resolution Reduced number of shades to one as not noticeable to human eye

Contains token numbers that represent letters Example: Storing “Station nation creation” tokens: “tion” = 1, “sta”=2, “na”=3, “crea”=4 so can be stored as ‘21 31 41’ which uses less data

RLE (image based) takes account that images have long runs of pixel with same colour. So if 3 or more consecutive of same colour, run of cells is encoded into 2 bytes

Advantages: Algorithm easy to implement and CPU does not require much horsepower Computer generated colour images give fair compression ratios Disadvantages: RLE only effective when file has lots of repetitive data

Always start with number of whites, then alternate between black and white

Instead of storing colour information about every pixel they store instructions about the image using less space.