Abstract: Image compression coding can not only effectively decrease the information redundancy among image’s pi-xels,but also ensure its reconstruction quality and lower computation complexity.The transform domain based image compression coding is one of the most commonly used and the best performanced compression technologies,but discrete orthogonal moments based image compression method has not yet been deeply studied.This paper studied the basis procedures of encoding and decoding of JPEG,and proposed an image compression algorithm based on discrete Tchebichef moments.We studied the distribution of the transformed coefficients by the approach of KS test statistic and designed the optimized quantization table by taking advantage of soft decision quantization to approximate the rate and the distortion for the purpose of improving reconstruction quality.Then,we encoded the results of quantization by using Huffman entropy coding.Finally,we realized the whole process of image compression and reconstruction based on discrete Tchebichef moments.Under the framework of JPEG baseline system,through comparing with the mainstream DCT image compression method,the experimental results show that the algorithm is of higher compression ratio when the bit ratio exceeds 0.5bpp.The compression performance of DTT is apparently superior to DCT when PSNR is 35dB,40dB,45dB respectively.Meanwhile,they are similar on the elapsed time in encoding and decoding.

Key words: Discrete Tchebichef moments,Soft decision quantization,JPEG,Image compression,Peak signal-to-noise ra-tio(PSNR)