Graph Algorithms
Information Retrieval: Computational and Theoretical Aspects
Information Retrieval: Computational and Theoretical Aspects
Storing text retrieval systems on CD-ROM: compression and encryption considerations
ACM Transactions on Information Systems (TOIS)
Storing text retrieval systems on CD-ROM: compression and encryption considerations
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
Is Huffman coding dead? (extended abstract)
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Bytecode compression via profiled grammar rewriting
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Skeleton Trees for the Efficient Decoding of Huffman Encoded Texts
Information Retrieval
Grammar-based compression of interpreted code
Communications of the ACM - Program compaction
Generation of fast interpreters for Huffman compressed bytecode
Science of Computer Programming - Special issue on advances in interpreters, virtual machines and emulators (IVME'03)
Efficient String Matching in Huffman Compressed Texts
Fundamenta Informaticae
Accelerating Boyer-Moore searches on binary texts
Theoretical Computer Science
Accelerating Boyer Moore searches on binary texts
CIAA'07 Proceedings of the 12th international conference on Implementation and application of automata
Efficient String Matching in Huffman Compressed Texts
Fundamenta Informaticae
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Although it is well-known that Huffman Codes are optimal for text compression in a character-per-character encoding scheme, they are seldom used in practical situations since they require a bit-per-bit decoding algorithm, which has to be written in some assembly language, and will perform rather slowly. A number of methods are presented that avoid these difficulties. The decoding algorithms efficiently process the encoded string on a byte-per-byte basis, are faster than the original algorithm, and can be programmed in any high level language. This is achieved at the cost of storing some tables in the internal memory, but with no loss in the compression savings of the optimal Huffman codes. The internal memory space needed can be reduced either at the cost of increased processing time, or by using non-binary Huffman codes, which give sub-optimal compression. Experimental results for English and Hebrew text are also presented.