用于数据压缩的快速自相关上下文模型
Fast Autocorrelated Context Models for Data Compression
摘要：
提出了一种通过计算数据的自相关函数自动生成数据上下文模型的方法。自相关函数的最大值出现在比特流中的偏移或滞后处，其倾向于与任何特定位置最高度相关。这些偏移对于在预测编码中使用是理想的，诸如用于数据压缩的预测部分匹配（PPM）或上下文混合算法，通过减少或消除对基于特定类型的数据的自组织模型的需要，使得这样的算法更有效且更通用。它没有使用自相关函数的定义，因为它考虑了数据的成对相关性，需要O（n^2）时间，而是应用了Weiner-Khinchin定理，在O（n log n）时间内快速获得自相关性作为数据功率谱的快速傅立叶逆变换，使该技术适用于压缩大数据对象。所示的方法，以产生最高水平的性能，迄今为止获得的无损图像压缩基准。
英文摘要：
A method is presented to automatically generate context models of data by calculating the data's autocorrelation function. The largest values of the autocorrelation function occur at the offsets or lags in the bitstream which tend to be the most highly correlated to any particular location. These offsets are ideal for use in predictive coding, such as predictive partial match (PPM) or context-mixing algorithms for data compression, making such algorithms more efficient and more general by reducing or eliminating the need for ad-hoc models based on particular types of data. Instead of using the definition of the autocorrelation function, which considers the pairwise correlations of data requiring O(n^2) time, the Weiner-Khinchin theorem is applied, quickly obtaining the autocorrelation as the inverse Fast Fourier transform of the data's power spectrum in O(n log n) time, making the technique practical for the compression of large data objects. The method is shown to produce the highest levels of performance obtained to date on a lossless image compression benchmark.
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。
