Text compression
Vector quantization and signal compression
Vector quantization and signal compression
Text algorithms
Pattern-matching and text-compression algorithms
ACM Computing Surveys (CSUR)
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
Pattern Matching Image Compression: Algorithmic and Empirical Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Information Theory and Data Compression
Introduction to Information Theory and Data Compression
Information Theory: Coding Theorems for Discrete Memoryless Systems
Information Theory: Coding Theorems for Discrete Memoryless Systems
Good Codes Based on Very Sparse Matrices
Proceedings of the 5th IMA Conference on Cryptography and Coding
Pattern Matching Image Compression with Predication Loop
DCC '97 Proceedings of the Conference on Data Compression
Source coding algorithms for fast data compression.
Source coding algorithms for fast data compression.
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Low Density Codes Achieve theRate-Distortion Bound
DCC '06 Proceedings of the Data Compression Conference
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
An Implementable Scheme for Universal Lossy Compression of Discrete Markov Sources
DCC '09 Proceedings of the 2009 Data Compression Conference
Generalized kraft inequality and arithmetic coding
IBM Journal of Research and Development
An introduction to arithmetic coding
IBM Journal of Research and Development
Nonlinear sparse-graph codes for lossy compression
IEEE Transactions on Information Theory
Modern Coding Theory
Simple universal lossy data compression schemes derived from the Lempel-Ziv algorithm
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A vector quantization approach to universal noiseless coding and quantization
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory - Part 1
A suboptimal lossy data compression based on approximate pattern matching
IEEE Transactions on Information Theory
Fixed-slope universal lossy data compression
IEEE Transactions on Information Theory
On the performance of data compression algorithms based upon string matching
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
An implementable lossy version of the Lempel-Ziv algorithm. I. Optimality for memoryless sources
IEEE Transactions on Information Theory
Pointwise redundancy in lossy data compression and universal lossy data compression
IEEE Transactions on Information Theory
Natural type selection in adaptive lossy compression
IEEE Transactions on Information Theory
Source coding, large deviations, and approximate pattern matching
IEEE Transactions on Information Theory
Tree encoding of memoryless time-discrete sources with a fidelity criterion
IEEE Transactions on Information Theory
Coding of sources with unknown statistics--II: Distortion relative to a fidelity criterion
IEEE Transactions on Information Theory
A 2-cycle algorithm for source coding with a fidelity criterion
IEEE Transactions on Information Theory
Trellis Encoding of memoryless discrete-time sources with a fidelity criterion
IEEE Transactions on Information Theory
Fixed rate universal block source coding with a fidelity criterion
IEEE Transactions on Information Theory
Time-invariant trellis encoding of ergodic discrete-time sources with a fidelity criterion
IEEE Transactions on Information Theory
A universal algorithm for sequential data compression
IEEE Transactions on Information Theory
Coding theorems for individual sequences
IEEE Transactions on Information Theory
Compression of individual sequences via variable-rate coding
IEEE Transactions on Information Theory
Distortion-rate theory for individual sequences
IEEE Transactions on Information Theory
Fixed data base version of the Lempel-Ziv data compression algorithm
IEEE Transactions on Information Theory
A coding theorem for lossy data compression by LDPC codes
IEEE Transactions on Information Theory
An algorithm for source coding subject to a fidelity criterion, based on string matching
IEEE Transactions on Information Theory
A survey of the theory of source coding with a fidelity criterion
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Construction and evaluation of trellis-coded quantizers for memoryless sources
IEEE Transactions on Information Theory
2D-pattern matching image and video compression: theory, algorithms, and experiments
IEEE Transactions on Image Processing
Achievable complexity-performance tradeoffs in lossy compression
Problems of Information Transmission
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The compression-complexity trade-off of lossy compression algorithms that are based on a random codebook or a random database is examined. Motivated, in part, by recent results of Gupta-Verdú-Weissman (GVW) and their underlying connections with the pattern-matching scheme of Kontoyiannis' lossy Lempel-Ziv algorithm, we introduce a nonuniversal version of the lossy Lempel-Ziv method (termed LLZ). The optimality of LLZ for memory-less sources is established, and its performance is compared to that of the GVW divide-and-conquer approach. Experimental results indicate that the GVW approach often yields better compression than LLZ, but at the price of much higher memory requirements. To combine the advantages of both, we introduce a hybrid algorithm (HYB) that utilizes both the divide-and-conquer idea of GVW and the single-database structure of LLZ. It is proved that HYB shares with GVW the exact same rate-distortion performance and implementation complexity, while, like LLZ, requiring less memory, by a factor which may become unbounded, depending on the choice of the relevant design parameters. Experimental results are also presented, illustrating the performance of all three methods on data generated by simple discrete memory-less sources. In particular, the HYB algorithm is shown to outperform existing schemes for the compression of some simple discrete sources with respect to the Hamming distortion criterion.