Journal of Algorithms
Data compression: methods and theory
Data compression: methods and theory
Journal of Computer and System Sciences
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
Introduction to Information Theory and Data Compression
Introduction to Information Theory and Data Compression
Advances in data compression and pattern recognition
Advances in data compression and pattern recognition
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Grammar-based codes: a new class of universal lossless source codes
IEEE Transactions on Information Theory
An adaptive character wordlength algorithm for data compression
Computers & Mathematics with Applications
A fast and efficient nearly-optimal adaptive Fano coding scheme
Information Sciences: an International Journal
| Hi-index | 0.00 |
Statistical coding techniques have been used for a long time in lossless data compression, using methods such as Huffman's algorithm, arithmetic coding, Shannon's method, Fano's method, etc. Most of these methods can be implemented either statically or adaptively. In this paper, we show that although Fano coding is sub-optimal, it is possible to generate static Fano-based encoding schemes which are arbitrarily close to the optimal, i.e. those generated by Huffman's algorithm. By taking advantage of the properties of the encoding schemes generated by this method, and the concept of "code word arrangement", we present an enhanced version of the static Fano's method, namely Fano+. We formally analyze Fano+ by presenting some pcoperties of the Fano tree, and the theory of list rearrangements. Our enhanced algorithm achieves compression ratios arbitrarily close to those of Huffman's algorithm on files of the Calgary corpus and the Canterbury corpus.