Multidimensional Signal Compression using Multi-scale Recurrent Patterns with Smooth Side-Match Criterion (WP-P1)

Author(s) : Eddie Filho (Genius Institute of Technology, Brazil) Murilo De Carvalho (TEC/CTC Universidade Federal Fluminense, Brazil) Eduardo Da Silva (Programa de Engenharia Elétrica/COPPE Universidade Federal do Rio de Janeiro, Brazil)

Abstract : The recently proposed method for image compression based on multi-scale recurrent patterns, the MMP (Multidimensional Multi-scale Parser) has been shown to perform well for a large class of images, specially for those containing text or graphics. However, its performance for coding smooth, gray scale images was still distant from the state-of-the art. In this paper we propose an extension for it, the SM-MMP (Side-match MMP). In it, as in MMP, a multidimensional signal is recursively segmented into variable-length blocks, 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 blocks. However, unlike MMP, in SM-MMP the dictionaries are built considering smoothness constraints around block boundaries, similarly to what happens in side-match vector quantization methods. This allows it to perform better than MMP when the images are smooth, without sacrificing its performance for images containing text or graphics. Indded, our simulation results show that the proposed method is effective, yielding improvements of the order of 1.5 dB over the original MMP for grayscale images, while preserving the high performance of the original MMP for graphics, text and mixed images.

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