Algorithms for adaptive Huffman codes
Information Processing Letters
Journal of Algorithms
Learning automata: an introduction
Learning automata: an introduction
Text compression
Introduction to Information Theory and Data Compression
Introduction to Information Theory and Data Compression
Grammar-based codes: a new class of universal lossless source codes
IEEE Transactions on Information Theory
Universal lossless source coding with the Burrows Wheeler transform
IEEE Transactions on Information Theory
A universal predictor based on pattern matching
IEEE Transactions on Information Theory
On delayed prediction of individual sequences
IEEE Transactions on Information Theory
On the performance of recency-rank and block-sorting universal lossless data compression algorithms
IEEE Transactions on Information Theory
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In this paper, we introduce a new approach to adaptive coding which utilizes Stochastic Learning-based Weak Estimation (SLWE) techniques to adaptively update the probabilities of the source symbols. We present the corresponding encoding and decoding algorithms, as well as the details of the probability updating mechanisms. Once these probabilities are estimated, they can be used in a variety of data encoding schemes, and we have demonstrated this, in particular, for the adaptive Fano scheme and and an adaptive entropy-based scheme that resembles the well-known arithmetic coding. We include empirical results using the latter adaptive schemes on real-life files that possess a fair degree of non-stationarity. As opposed to higher-order statistical models, our schemes require linear space complexity, and compress with nearly 10% better efficiency than the traditional adaptive coding methods.