In this paper, we propose an entropy minimization histogram mergence (EMHM) scheme that can significantly reduce the number of grayscales with nonzero pixel populations (GSNPP) without visible loss to image quality. We proved in theory that the entropy of an image is reduced after histogram mergence and that the reduction in entropy is maximized using our EMHM. The reduction in image entropy is good for entropy encoding considering that the minimum average code word length per source symbol is the entropy of the source signal according to Shannon’s first theorem. Extensive experimental results show that our EMHM can significantly reduce the code length of entropy coding, such as Huffman, Shannon, and arithmetic coding, by over 20% while preserving the image subjective and objective quality very well. Moreover, the performance of some classic lossy image compression techniques, such as the Joint Photographic Experts Group (JPEG), JPEG2000, and Better Portable Graphics (BPG), can be improved by preprocessing images using our EMHM.

在本文中，我们提出了一种熵最小化直方图合并 (EMHM) 方案，该方案可以显着减少具有非零像素群 (GSNPP) 的灰度数量，而不会造成图像质量的明显损失。我们在理论上证明了在直方图合并后图像的熵减少了，并且使用我们的 EMHM 使熵的减少最大化。根据香农第一定理，考虑到每个源符号的最小平均码字长度是源信号的熵，图像熵的减少有利于熵编码。大量的实验结果表明，我们的 EMHM 可以显着减少熵编码（例如 Huffman、Shannon 和算术编码）的码长 20% 以上，同时很好地保留了图像的主客观质量。而且，