We previously proposed a novel lossless image coding method that utilizes example search and adaptive prediction within a framework of probability model optimization. In this paper, the definition of the probability model as well as its optimization procedure are modified to reduce the encoding complexity. In addition, affine predictors used in the adaptive prediction are refined for accurate probability modeling. Simulation results indicate that our modification contributes not only to encoding time reduction, but also to coding efficiency improvement for all of the tested images.
We previously proposed a lossless video coding method based on intra/inter-frame example search and probability model optimization. In this method, several examples, i.e. a set of pels whose neighborhoods are similar to a local texture of the target pel to be encoded, are searched from already encoded areas of the current and previous frames with integer pel accuracy. Probability distribution of an image value at the target pel is then modeled as weighted sum of the Gaussian functions whose peaked positions are given by the individual examples. Furthermore, model parameters that control shapes of the Gaussian functions are numerically optimized so that the resulting coding rate can be a minimum. In this paper, the above example search process is enhanced to allow fractional-pel positions for more accurate probability modeling.
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