Entropy Conserving Binarization Scheme for Video and Image Compression

TL;DR

This paper introduces an entropy-conserving binarization scheme for converting non-binary data into binary form, which is effective regardless of the data's probability distribution, and suitable for integration into compression algorithms.

Contribution

The paper proposes a novel binarization scheme that conserves entropy for any source distribution without prior knowledge, enhancing data compression efficiency.

Findings

Conserves entropy for any data distribution

Linear complexity in data length

Applicable to various compression algorithms

Abstract

The paper presents a binarization scheme that converts non-binary data into a set of binary strings. At present, there are many binarization algorithms, but they are optimal for only specific probability distributions of the data source. Overcoming the problem, it is shown in this paper that the presented binarization scheme conserves the entropy of the original data having any probability distribution of $m$-ary source. The major advantages of this scheme are that it conserves entropy without the knowledge of the source and the probability distribution of the source symbols. The scheme has linear complexity in terms of the length of the input data. The binarization scheme can be implemented in Context-based Adaptive Binary Arithmetic Coding (CABAC) for video and image compression. It can also be utilized by various universal data compression algorithms that have high complexity in compressing non-binary data, and by binary data compression algorithms to optimally compress non-binary data.