 luminance (brightness) and colour (chrominance). Pixels behave very similarly to people. There are black pixels and white pixels and 254 shades of grey pixel in between. Pixels of similar colour tend to congregate together. A white pixel in a predominantly black pixel area stands out as being a bit unusual (an outlier). There are good pixels and bad pixels. Some bad pixels start life as good pixels but become bad later in life. Noisy pixels can be a big problem not only to themselves but also to their Information-Centric Software neighbours. Pixels see life through red, green, blue or clear spectacles but lack the natural colour-balancing properties of the human visual system. Their behaviour is, however, fairly predictable both spatially in a still image and temporally in a moving image. 10.3.2 The Big Decision In RF signal processing, we have to take a final decision as to whether a 0 is a 0 or a 1 is a 1 or whether a bit represents a +1 or - 1 (See Chapter 3 section on matrix maths). In the same way, a decision has to be taken in the image pipe - is this a black pixel or a white pixel? (or one of 254 shades of pixel in between) or a green, red or blue pixel of a certain intensity and should that pixel be there or actually moved along a bit. Given that pixels are arranged in 4 by 4 or 8 by 8 or 16 by 16 macroblocks this is like a game of suduko, or preferred a slightly complicated form of noughts and crosses (diagonals are important in the pixel world). Pixels that do not fit a given set of rules can be changed to meet the rules. These decisions can be based on comparing a point pixel value with that of its neighbours. Simplistically, it is this principle that is applied to image enhancement in the image pipe (noise reduction, blur reduction, edge sharpening) and to image manipulation either in or after the image pipe (colour balancing, etc.). 10.3.3 Clerical Mathematicians who Influenced the Science of Pixel Probability This gives us an excuse to introduce three mathematicians, the Reverend Thomas Bayes (the 