Data compression: methods and theory
Data compression: methods and theory
Information Sciences: an International Journal - Dictionary based compression
2D-Pattern Matching Image and Video Compression
DCC '99 Proceedings of the Conference on Data Compression
Overlap and channel errors in adaptive vector quantization for image coding
Information Sciences—Informatics and Computer Science: An International Journal
2D-pattern matching image and video compression: theory, algorithms, and experiments
IEEE Transactions on Image Processing
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Textual substitution methods are one-dimensional compression methods that maintain a constantly changing dictionary of strings to adaptively compress a stream of characters by replacing common substrings with indices (pointers) into a dictionary. Lempel and Ziv proved that the proposed schemes were practical as well as asymptotically optimal for a general source model. Two-dimensional (i.e. images) applications of textual substitution methods have been widely studied in the past. Substantially those applications involve first the application of a linearization strategy to the input data, and then the encoding of the resulting monodimensional vector using LZ type one dimensional methods. In this paper we discuss the textual substitution methods for image compression, with particular attention to the AVQ class of algorithms, and review recent advances in the field.