Image Representation

Objectives  Bitmaps: resolution, colour depth and simple bitmap file calculations.  Vector graphics: drawing list – objects and their properties.  Compare bitmaps to vector graphics, advantages, disadvantages

Bitmap images  These are images that are split into lots of different squares  Each square is called a pixel If two or more colours are split over a pixel the colours are blended Each colour has a different binary pattern to represent it

Resolution  This is the term given to the number of pixels being used  Mainly used to express the size of a screen  Described by stating the number of pixels per row and columns  Some common resolutions are:  1024 x 798  800 x 600  640 x 480  It’s important to remember that it’s not the number of pixels that determines the sharpness, it’s the size of the pixels – smaller the better Challenge Work out the total pixels in each of the common resolutions

Colour Depth  All pictures have to specify the number of colours that are required – this is called colour depth  Colour depth is represented in bits, the more bits the more colours available 1 bit 2 Bits4 Bits 2 Possible values 0 = Black 1 = White 4 Possible values 00 = Black 01 = Dark Grey 10 = Light Grey 11 = White 16 Possible values 0000 = Red 0001 = Orange …… 1110 = Indigo 1111 = Violet

Colour Depth 2  There are a few standards with colour depth, as it would become very cluttered otherwise  1 Bit Colour Maximum of two different colours and generally used for black and white  12 bit Colour 4 bits are used for each of the R, G and B components. So that means that you can have 4096 different colours (16 x 16 x 16)  True Colour 8 bits are used for each R, G and B component. This allows just over 16.7 million different colours (256 x 256 x 256).  32 bit colour Very similar to true colour, however the final 8 bits are used for transparency. Challenge Calculate the minimum file size of a 1024 x 768 pixel image that uses a 24 bit colour depth

Colour Depth 2  There are a few standards with colour depth, as it would become very cluttered otherwise  1 Bit Colour Maximum of two different colours and generally used for black and white  12 bit Colour 4 bits are used for each of the R, G and B components. So that means that you can have 4096 different colours (16 x 16 x 16)  True Colour 8 bits are used for each R, G and B component. This allows just over 16.7 million different colours (256 x 256 x 256).  32 bit colour Very similar to true colour, however the final 8 bits are used for transparency.

Vector Images  There’s NO pixels or breaking up of images in vector images  Instead they are objects that have information such as width, height and length  The information is called a vector  On the positive side, the file size is a lot smaller as you just need a few bits to describe a vector, in comparison to each pixel.  The picture doesn't distort or pixilate as you zoom in

Vector Images 2  In order to create the image a drawing list is created.  They tell the program what to draw and the size and position  Mathematics is used to create round shapes Drawing listExplanation Line(20,10,180,20,red,4)Draw line from 20,10 to 180,20 in red, 4 pixels wide rect(90,50,90,140,red,filled,none)Draw rectangle top left 90,50 bottom right 90,140 filled red, no border Circle(30,80,10,red,filled,white)Draw circle from 30,80 radius 10 filled in red, white border

Compression Run Length Encoding  Also know as Lossless compression; as the name suggests no data is lost in this process  The picture is scanned for long strings of the same colour and a substitution is used  Example files – GIF, JPEG & PNG Example of Encoding Lossy Compression Count of identical colours Binary code for colour  Lossy compression is where data considered less important is discarded  Once the data is discarded its lost completely.  You are unable to recover the file  Lossy creates smaller files than lossless