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Resolution-scalable lossless image compression methods are indispensable for medical imaging and digital archives for example. Therefore, we have developed hierarchical lossless image compression methods using cellular neural network (CNN) predictors. In this method, CNN predictors are assigned for each pixel and their shapes and assignment are optimized to maximize the compression ratio of a given image. In this research, we propose a recompressible hierarchical lossless image compression scheme using CNN predictors optimized by the particle swarm optimization with a refractory period (PSO-RP). The main advantage of the proposed method is that it can compress images quickly using CNN predictors given in an earlier phase of optimization and that recompression is possible via the proposed compression framework. The results of a compression experiment clarified that the compression performance of the proposed method is gradually improved in proportion to the number of repetitions of optimization, and very high compression performance is archived with little performance degradation due to recompression.

journal of signal processing

Recompressible Hierarchical Lossless Image Compression Scheme Using Pel-Adaptive Cellular Neural Network Predictors Optimized by PSO with a Refractory Period