Title :
Text compression via alphabet re-representation
Author :
Long, Philip M. ; Natsev, Apostol I. ; Vitter, Jeffrey Scott
Author_Institution :
Dept. of Inf. Syst. & Comput. Sci., Nat. Univ. of Singapore, Singapore
Abstract :
We consider re-representing the alphabet so that a representation of a character reflects its properties as a predictor of future text. This enables us to use an estimator from a restricted class to map contexts to predictions of upcoming characters. We describe an algorithm that uses this idea in conjunction with neural networks. The performance of this implementation is compared to other compression methods, such as UNIX compress, gzip, PPMC, and an alternative neural network approach
Keywords :
backpropagation; data compression; document image processing; feedforward neural nets; image coding; image representation; multilayer perceptrons; neural net architecture; prediction theory; word processing; PPMC; UNIX compress; algorithm; alphabet rerepresentation; compression methods; contexts; data compression systems; estimator; feedforward multilayer backpropagation neural network; gzip; neural network architecture; performance; text compression; upcoming character prediction; Arithmetic; Computer science; Convergence; Encoding; Information systems; Multidimensional systems; Neural networks; Probability distribution; State estimation; Text processing;
Conference_Titel :
Data Compression Conference, 1997. DCC '97. Proceedings
Conference_Location :
Snowbird, UT
Print_ISBN :
0-8186-7761-9
DOI :
10.1109/DCC.1997.582003
