Quantizacao Vetorial Adaptativa Multiescalas com Otimização Taxa-Distorção
In this paper we propose a new multidimensional lossy sigual compression method based on multiscale recurrent patterns, referred to as MMP (Multidimensional Multiscale Parser). In it, a multidimensional signal is recursively segmented into variable-length vectors, and each segment is encoded using expansions and contractions of vectors in a dictionary. The dictionary is updated while the data is being encoded, using concatenations of expanded and contracted versions of previously encoded vectors. The only data encoded are the segmentation tree and the indexes of the vectors in the dictionary, and therefore no side information is necessary for the dictionary updating. The signal segmentation is carried out through a rate-distortion optimization procedure. A two-dimensional version of the MMP algorithm was implemented and tested with several kinds of image data. We have observed that the proposed dictionary updating procedure is effective in adapting the algorithm to a large variety of image content, lending to it a universal flavor. For text and graphics images, it outperforms the state-of-the-art SPIHT algorithm by more that 3dB at 0.5bpp, while for mixed document images, containing text, graphics and gray scale images, by more than 1.5dB at the same rate. We conclude the paper with a theoretical analysis of the approximate matching of gaussian vectors using scales, which gives a justification of why approximate multiscale matching is a good option, specially
at low rates.
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