A fast and efficient nearly-optimal adaptive Fano coding scheme

Rueda, Luis; Oommen, B. John

doi:10.1016/j.ins.2005.07.010

Cited by 16 publications

(16 citation statements)

References 26 publications

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“…Practical experiments in [15] demonstrate that their method compares favorably to an implementation of the FGK algorithm. Turpin and Moffat [19] describe an algorithm GEO for adaptive prefix coding.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Algorithm for Adaptive Prefix Coding

Karpiński

Nekrich

2007

Algorithmica

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In this paper we present a new algorithm for adaptive prefix coding. Our algorithm encodes a text S of m symbols in O(m) time, i.e., in O(1) amortized time per symbol. The length of the encoded string is bounded above by (H + 1)m + O(n log 2 m) bits where n is the alphabet size and H is the entropy.This is the first algorithm that adaptively encodes a text in O(m) time and achieves an almost optimal bound on the encoding length in the worst case. Besides that, our algorithm does not depend on an explicit code tree traversal.

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Section: Introductionmentioning

confidence: 99%

“…Rueda and Oommen [15] describe an algorithm for adaptive prefix coding based on the adaptive variant of the Fano coding [16,17]. Practical experiments in [15] demonstrate that their method compares favorably to an implementation of the FGK algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Fast Algorithm for Adaptive Prefix Coding

Karpiński

Nekrich

2007

Algorithmica

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“…However, the compression ratio based on adaptive coding provides nearly the same performance as the other algorithms. Table 9 compares the compression ratio of the new ACW(n,s) scheme with a number of adaptive schemes as follows: the Unix compact utility based on adaptive Huffman (AH), the greedy adaptive Fano coding (AF) [23] , and the HCDC(k) scheme [6] . The compression ratio of the ACW(n,s) scheme based on Huffman coding is higher than all of other schemes for all text files examined.…”

Section: Resultsmentioning

confidence: 99%

“…The coding rate of the new scheme is found to be competing with many standard softwares, and it is better if the ACW(n, s) uses Huffman coding in converting text into binary sequence. [23] AF [23] HCDC(k) scheme [6] ACW(n,s) scheme …”

Section: Methodsmentioning

confidence: 99%

A bit-level text compression scheme based on the ACW algorithm

Al-Bahadili

Hussain

2010

Int. J. Autom. Comput.

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This paper presents a description and performance evaluation of a new bit-level, lossless, adaptive, and asymmetric data compression scheme that is based on the adaptive character wordlength (ACW(n)) algorithm. The proposed scheme enhances the compression ratio of the ACW(n) algorithm by dividing the binary sequence into a number of subsequences (s), each of them satisfying the condition that the number of decimal values (d) of the n-bit length characters is equal to or less than 256. Therefore, the new scheme is referred to as ACW(n, s), where n is the adaptive character wordlength and s is the number of subsequences. The new scheme was used to compress a number of text files from standard corpora. The obtained results demonstrate that the ACW(n, s) scheme achieves higher compression ratio than many widely used compression algorithms and it achieves a competitive performance compared to state-of-the-art compression tools.

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“…We also ran the greedy adaptive Fano coding presented in [36] and the AHC algorithm [27] on the same benchmarks. In the subsequent tables, the first column contains the name of the file.…”

Section: Resultsmentioning

confidence: 99%

An efficient compression scheme for data communication which uses a new family of self‐organizing binary search trees

Rueda

Oommen

2008

Int J Communication

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SUMMARYIn this paper, we demonstrate that we can effectively use the results from the field of adaptive selforganizing data structures in enhancing compression schemes. Unlike adaptive lists, which have already been used in compression, to the best of our knowledge, adaptive self-organizing trees have not been used in this regard. To achieve this, we introduce a new data structure, the partitioning binary search tree (PBST) which, although based on the well-known binary search tree (BST), also appropriately partitions the data elements into mutually exclusive sets. When used in conjunction with Fano encoding, the PBST leads to the so-called Fano binary search tree (FBST), which, indeed, incorporates the required Fano coding (nearly equal probability) property into the BST. We demonstrate how both the PBST and the FBST can be maintained adaptively and in a self-organizing manner. The updating procedure that converts a PBST into an FBST, and the corresponding new tree-based operators, namely the shift-to-left and the shift-to-right operators, are explicitly presented. The encoding and decoding procedures that also update the FBST have been implemented and rigorously tested. Our empirical results on the files of the well-known benchmarks, the Calgary and Canterbury Corpora, show that the adaptive Fano coding using FBSTs, the Huffman, and the greedy adaptive Fano coding achieve similar compression ratios. However, in terms of encoding/decoding speed, the new scheme is much faster than the latter two in the encoding phase, and they achieve approximately the same speed in the decoding phase. We believe that the same philosophy, namely that of using an adaptive self-organizing BST to maintain the frequencies, can also be utilized for other data encoding mechanisms, even as the Fenwick scheme has been used in arithmetic coding.

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A fast and efficient nearly-optimal adaptive Fano coding scheme

Cited by 16 publications

References 26 publications

A Fast Algorithm for Adaptive Prefix Coding

A Fast Algorithm for Adaptive Prefix Coding

A bit-level text compression scheme based on the ACW algorithm

An efficient compression scheme for data communication which uses a new family of self‐organizing binary search trees

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