Pattern Matching Image Compression: Algorithmic and Empirical Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional signal compression using multiscale recurrent patterns
Signal Processing - Image and Video Coding beyond Standards
Adaptive Parametric Vector Quantization by Natural Type Selection
DCC '02 Proceedings of the Data Compression Conference
Fast gapped variants for Lempel--Ziv--Welch compression
Information and Computation
IEEE Transactions on Information Theory
Complexity-compression tradeoffs in lossy compression via efficient random codebooks and databases
Problems of Information Transmission
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Lossless and lossy data compression algorithms based on string matching are considered. In the lossless case, a result of Wyner and Ziv (1989) is extended. In the lossy case, a data compression algorithm based on approximate string matching is analyzed in the following two frameworks: (1) the database and the source together form a Markov chain of finite order; (2) the database and the source are independent with the database coming from a Markov model and the source from a general stationary, ergodic model. In either framework, it is shown that the resulting compression rate converges with probability one to a quantity computable as the infimum of an information theoretic functional over a set of auxiliary random variables; the quantity is strictly greater than the rate distortion function of the source except in some symmetric cases. In particular, this result implies that the lossy algorithm proposed by Steinberg and Gutman (1993) is not optimal, even for memoryless or Markov sources