Higher-resolution pictures have more detail and in particular lines are smoother. The image is less pixelated – you see the squares less. It also means you can see more fine detail – smaller things may disappear in a low resolution image.Įach image has to also come with (or have pre-agreed) a key indicating which number corresponds to which colour. The number of colours allowed is called the colour depth. The more colours, the more bits are needed to store each pixels colours. With two colours you need a single bit for each pixel, with 4 colours you need 2 bits, with 8 colours you need 3 bits, and so on. Each bit pattern represents a different number and so a different colour.Īn important part of computational thinking involves being able to choose an appropriate representation of data. It is important to know about different representations already used. Choosing representations is a part of abstraction: choosing what matters to represent about data and what can be ignored. With bitmap images, part of that is in choosing the resolution. By splitting the image in to small squares and ignoring finer detail, we get an easy way to store, manipulate and transmit images. Once the image is a list of numbers we can explore variations of the representation that allow us to compress the image – store it using fewer numbers. This is also an example of decomposition with respect to data. The image is decomposed in to small squares. A different way to decompose an image, so a different representation is by the lines and shapes within it. This decomposition instead leads instead to vector images.Įvery image is unique, but by choosing a representation of bitmap images we get a generalised way to represent an image. Instructions: Simple Colour-by-number PuzzlesĮach square holds a number that tells you the colour to colour in that square. Look up the colours in the key.Įxplore different algorithms for colouring them in.
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