SUMMARYA highly efficient lossless encoding method for static images is proposed. In this method, multiple linear predictors are created for each image and adaptive prediction that responds to the local structure of images such as edges and textures is achieved by switching between these predictors at the block level. Furthermore, the probability density functions of the prediction errors are categorized by context modeling and modeled by generalized Gaussian functions, and adaptive arithmetic encoding of the prediction errors is performed by using probability tables that are generated for each pixel from this model. Parameters that are needed in the coding such as the prediction coefficients, the predictor selection data for each block, and the shapes of the generalized Gaussian functions are optimized by repeatedly minimizing a cost function that includes the code length of the parameters themselves in addition to the code length of the prediction errors that are calculated from the probability model above, and the parameters are then encoded separately as side data for each image. A procedure is introduced to improve prediction accuracies by using quadtree segmentation to segment the image into variable-sized blocks between which the predictor can change. Coding experiments are conducted and the proposed method is found to produce coding rates of 6 to 44% lower than the international standard JPEG-LS method, with the proposed method achieving superior coding performance that surpasses existing coding methods for all of the images used in the experiments.

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