Genome Sequence Compression based on Entropy Coding with Context Quantization and spatial co-location pattern

Entropy coding based on Context quantization has widely used in image, video data compression, and genome sequence compression. The main problem of the applications is how to establish an effective Context model, which not only improve entropy encoding efficiency but also reduce the model cost. Context quantization is effective to solve this problem. However, the current genome sequence compression methods used are based on experience for design, and can not achieve optimal coordination with the source. This paper based on the spatial co-location pattern and optimal Context quantization could improve the accuracy and optimizes the Context quantization. The entropy encoding based on Context modeling constructs a Context quantification genome sequence compression model with minimal human intervention.