一种用于视频和图像压缩的熵保持二值化方法
Entropy Conserving Binarization Scheme for Video and Image Compression
摘要：
本文提出了一种二进制化方案，将非二进制数据转换为一组二进制字符串。目前，有许多二值化算法，但它们仅适用于数据源的特定概率分布。克服了这个问题，本文表明，所提出的二值化方案保存的原始数据的熵具有任何概率分布的$m$ -ary源。该方案的主要优点是在不知道信源和信源符号概率分布的情况下保持了熵。该方案的复杂度与输入数据的长度成线性关系。二进制化方案可以在用于视频和图像压缩的基于上下文的自适应二进制算术编码（CABAC）中实现。它还可以被各种通用的数据压缩算法利用，这些算法在压缩非二进制数据时具有高复杂度，并且还可以被二进制数据压缩算法利用来最佳地压缩非二进制数据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
