In this paper, the most efficient (from data compaction point of view) and current image lossless coding method is presented. Being computationally complex, the algorithm is still more time efficient than its main competitors. The presented cascaded method is based on the Weighted Least Square (WLS) technique, with many improvements introduced, e.g., its main stage is followed by a two-step NLMS predictor ended with Context-Dependent Constant Component Removing. The prediction error is coded by a highly efficient binary context arithmetic coder. The performance of the new algorithm is compared to that of other coders for a set of widely used benchmark images.
In this paper the basics of data predictive modeling (using the method of minimization mean square error) for lossless audio compression are presented. The described research focuses on inter-channel analysis and setting range of prediction in dependencies of frame size. In addition, the concept of data flow using inter-channel dependencies and an authorial, effective and flexible method of saving prediction coefficients are presented.
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