Artifact reduction of JPEG coded images using mean-removed classified vector quantization

Abstract

Image compression techniques are frequently applied to reduce the network bandwidth and storage space. In the case of higher compression ratios, annoying artifacts may be generated and they degrade the perceptual quality of compressed images. This paper modified mean-removed classified vector quantization (MRCVQ) to reduce the artifacts of JPEG coded images. This algorithm consists of four phases: mean removal, encoding, decoding, and mean restoration. The mean removal phase removes the mean values of compressed image blocks. The encoding procedure needs a codebook for the encoder, which transforms a mean-removed compressed image to a set of codeword-indices. The decoding phase requires a different codebook for the decoder, which enhances a mean-removed compressed image from a set of codeword-indices. Finally, the mean values are restored in the mean restoration phase. The experimental results show that the proposed approach can remove effectively the artifacts caused by high compression and improve the perceptual quality significantly. Compared to the existing methods, our approach usually has the much better performance in terms of computing time, storage space and PSNR.