A universal algorithm for sequential data compression

Universal Data Compression and Linear Prediction

The relationship between prediction and data compression can be extended to universal prediction schemes and universal data compression. Recent work shows that minimizing the sequential squared prediction error for individual sequences can be achieved using the same strategies which minimize the sequential codelength for data compression of individual sequences. De ning a \probability" as an ex...

Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform .2. With context models

Efficient universal lossless data compression algorithms based on a greedy sequential grammar transform - Part one: Without context models

A grammar transform is a transformation that converts any data sequence to be compressed into a grammar from which the original data sequence can be fully reconstructed. In a grammar-based code, a data sequence is first converted into a grammar by a grammar transform and then losslessly encoded. In this paper, a greedy grammar transform is first presented; this grammar transform constructs sequ...

Sequential decoding for lossless streaming source coding with side information

The problem of lossless fixed-rate streaming coding of discrete memoryless sources with side information at the decoder is studied. A random time-varying tree-code is used to sequentially bin strings and a Stack Algorithm with a variable bias uses the side information to give a delay-universal coding system for lossless source coding with side information. The scheme is shown to give exponentia...

Universal Linear Prediction by Model Order

A common problem that arises in adaptive ltering, autoregressive modeling, or linear prediction is the selection of an appropriate order for the underlying linear parametric model. We address this problem, but instead of xing a speciic model order we develop a sequential algorithm whose sequentially accumulated mean squared prediction error for any bounded individual sequence is as good as the ...