Why Shape Coding? Asymptotic Analysis of the Entropy Rate for Digital Images

Abstract

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 $O(\frac{1}{\log t})$. 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 $O(\frac{1}{\log t})$ 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.

References in corpus (1)

• EXK-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision