Principles and practice of information theory
Principles and practice of information theory
Multirate systems and filter banks
Multirate systems and filter banks
Pattern Matching Image Compression: Algorithmic and Empirical Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
MPEG Video Compression Standard
MPEG Video Compression Standard
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
A suboptimal lossy data compression based on approximate pattern matching
IEEE Transactions on Information Theory
Quantized overcomplete expansions in IRN: analysis, synthesis, and algorithms
IEEE Transactions on Information Theory
On the performance of data compression algorithms based upon string matching
IEEE Transactions on Information Theory
An implementable lossy version of the Lempel-Ziv algorithm. I. Optimality for memoryless sources
IEEE Transactions on Information Theory
2D-pattern matching image and video compression: theory, algorithms, and experiments
IEEE Transactions on Image Processing
Layered image coding using the DCT pyramid
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Improving multiscale recurrent pattern image coding with least-squares prediction mode
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Scanned compound document encoding using multiscale recurrent patterns
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
H.264/AVC based video coding using multiscale recurrent patterns: first results
VLBV'05 Proceedings of the 9th international conference on Visual Content Processing and Representation
A generic post-deblocking filter for block based image compression algorithms
Image Communication
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In this paper we propose a new multidimensional signal lossy compression method based on multiscale recurrent patterns, referred to as multidimensional multiscale parser (MMP). 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 3 dB at 0.5 opp, while for mixed document images, containing text, graphics and gray-scale images, by more than 1.5 dB at the same rate. Due to the way the images are segmented, they appear slightly blocky at low rates. We have alleviated this problem by proposing an effective way of reducing the blockiness in the reconstructed image, with no penalty in signal-to-noise ratio performance in most cases. 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.