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Human visual system-based block classification algorithm for image sequence coders

For a long time, most of the hybrid codecs for moving images have been of a block based type. Each block is then coded rather independently by spending bits according to its statistically relevant contents. But this does not yet mean that the data is visually relevant. To overcome this, sometimes people incorporate some visual parameters inside their coding process. The visual weighting matrix so often used in DCT coding is such an example. However, this approach often results in complications, since the coding algorithm as such is almost never suitable to extract or interpret visual relevance. For instance, DCT coefficients do not represent a visual phenomenon. The problem can be dealt with more thoroughly by completely separating the process into a visual analysis step and a coding step. An analysis step can now assign attributes to visual features, irrespectively of the actual coding algorithm. Its results can then be used by the coding engine to choose a suitable algorithm, to adapt parameters or to choose a local quality criterion. In this way, it is possible to code better adapted to local visual features. This paper will describe a codec using such a classification algorithm and present some results.