基于最优预测、自适应提升和条件算术编码的无损图像压缩。

Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding.

机构信息

Information Processing Laboratory, Department of Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, Thessaloniki 54006, Greece.

出版信息

IEEE Trans Image Process. 2001;10(1):1-14. doi: 10.1109/83.892438.

DOI:10.1109/83.892438

PMID:18249592

Abstract

The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column sampling. In each case, the efficiency of the linear predictors is enhanced nonlinearly. Directional postprocessing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely efficient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.

摘要

在一般的 n 维情况下，获得了提升方案的最佳预测器，并将其应用于使用第一五重采样然后简单的行-列采样的无损压缩。在每种情况下，线性预测器的效率都通过非线性方式得到增强。在五重采样的情况下使用了方向后处理，在行-列采样的情况下使用了自适应长度后处理。这两种方法都表现良好。所得到的非线性插值方案实现了非常有效的图像去相关。我们进一步研究了无损压缩框架中的小波系数的上下文建模和自适应算术编码。特别关注建模上下文和算术编码器对实际数据的自适应。实验评估表明，所得到的编码器中最好的一个比其他用于基于多分辨率的无损图像编码的已知算法产生更好的结果。