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

Semantic communication is not obsessed with improving the accuracy of transmitted symbols, but is concerned with expressing the desired meaning that the symbol sequence exactly carried. However, the generation and measurement of semantic messages are still an open problem. Expansions combine simple things into complex systems and even generate intelligence, which is consistent with the evolution of the human language system. We apply this idea to semantic communication system, quantifying and transmitting semantics by symbol sequences, and investigate the semantic information system in a similar way as Shannon did for digital communication systems. This work was the first to propose the concept of semantic expansion and knowledge collision, which may provide a new paradigm for semantic communications. We believe that expansions and collisions will be the cornerstone of semantic information theory.

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

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.

Soft Compression for Lossless Image Coding

Soft compression is a lossless image compression method, which is committed to eliminating coding redundancy and spatial redundancy at the same time by adopting locations and shapes of codebook to encode an image from the perspective of information theory and statistical distribution. In this paper, we propose a new concept, compressible indicator function with regard to image, which gives a threshold about the average number of bits required to represent a location and can be used for revealing the performance of soft compression. We investigate and analyze soft compression for binary image, gray image and multi-component image by using specific algorithms and compressible indicator value. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images can be greatly reduced by applying soft compression.