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
Achieving the rate-distortion bound with low-density generator matrix codes
IEEE Transactions on Communications
Bridging lossy and lossless compression by motif pattern discovery
General Theory of Information Transfer and Combinatorics
Achievable complexity-performance tradeoffs in lossy compression
Problems of Information Transmission
Complexity-compression tradeoffs in lossy compression via efficient random codebooks and databases
Problems of Information Transmission
| Hi-index | 754.84 |
A new lossy variant of the fixed-database Lempel-Ziv coding algorithm for encoding at a fixed distortion level is proposed, and its asymptotic optimality and universality for memoryless sources (with respect to bounded single-letter distortion measures) is demonstrated: as the database size m increases to infinity, the expected compression ratio approaches the rate-distortion function. The complexity and redundancy characteristics of the algorithm are comparable to those of its lossless counterpart. A heuristic argument suggests that the redundancy is of order (log log m)/log m, and this is also confirmed experimentally; simulation results are presented that agree well with this rate. Also, the complexity of the algorithm is seen to be comparable to that of the corresponding lossless scheme. We show that there is a tradeoff between compression performance and encoding complexity, and we discuss how the relevant parameters can be chosen to balance this tradeoff in practice. We also discuss the performance of the algorithm when applied to sources with memory, and extensions to the cases of unbounded distortion measures and infinite reproduction alphabets