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SUMMARYIn this paper, the authors propose a new method for designing predictors suitable for lossless image coding. In recent years, lossless coding systems based on optimal design of predictors for each image have been studied. In these systems, the linear prediction coefficients are determined so as to minimize the mean squared prediction errors. In lossless image coding, however, where the ultimate goal is to reduce the coding rate, minimizing the mean squared prediction errors does not necessarily yield the best results. Therefore, in order to reduce the coding rate directly, the authors attempted to formulate the amount of information on the prediction errors and design the predictors so as to minimize that value. Moreover, the image is divided into blocks, and these blocks are classified into multiple classes; multiple predictors for adaptive prediction are optimized at the same time by repeatedly executing the design of the predictor for each class and updating the class for each block, based on the cost representing the amount of information. In the results from a coding simulation, this system demonstrated superior coding efficiency compared to the conventional method for minimizing the mean squared prediction errors, and was confirmed to achieve a coding rate of 0.37 bit/pel lower than JPEG-LS which is an international standard for lossless image coding.

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