论文标题
为什么要形状编码？数字图像熵率的渐近分析
Why Shape Coding? Asymptotic Analysis of the Entropy Rate for Digital Images
论文作者
论文摘要
本文着重于图像压缩的最终极限理论。它证明，对于图像源，存在具有形状的编码方法，该方法可以在一定条件下实现熵率，而编码器/解码器中的形状像素比为$ O（{1 \ over {\ log log t}}）$。基于新发现，提出了具有形状的图像编码框架，并证明对固定和赤道过程均无最佳。此外，在图像数据库MNIST中已经确认了编码器/解码器中形状像素比的条件$ O（{1 \ over {\ log t t}}）$ $ o（{\ log t}}）$，这说明了使用形状编码的软压缩是一种近乎最佳的图像损失压缩的方案。
This paper focuses on the ultimate limit theory of image compression. It proves that for an image source, there exists a coding method with shapes that can achieve the entropy rate under a certain condition where the shape-pixel ratio in the encoder/decoder is $O({1 \over {\log t}})$. Based on the new finding, an image coding framework with shapes is proposed and proved to be asymptotically optimal for stationary and ergodic processes. Moreover, the condition $O({1 \over {\log t}})$ of shape-pixel ratio in the encoder/decoder has been confirmed in the image database MNIST, which illustrates the soft compression with shape coding is a near-optimal scheme for lossless compression of images.
