Image restoration of compressed image using classified vector quantization
Pattern RecognitionPublished 1 February 2002
Yi‐Ching Liaw, Winston Lo, Jim Z.C. Lai
Citations54
SJR quartileQ1
SJR score2.06
SNIP2.67
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TL;DR
This paper extends and modifies classified vector quantization (CVQ) to improve the quality of compressed images and shows that the image quality is improved dramatically.
Abstract
To reduce communication bandwidth or storage space, image compression is needed. However, the subjective quality of compressed images may be unacceptable and the improvement of quality for compressed images may be desirable. This paper extends and modifies classified vector quantization (CVQ) to improve the quality of compressed images. The process consists of two phases: the encoding phase and the decoding phase. The encoding procedure needs a codebook for the encoder, which transforms a compressed image to a set of codeword-indices. The decoding phase also requires a different codebook for the decoder, which enhances a compressed image from a set of codeword-indices. Using CVQ to improve a compressed image's quality is different from the existing algorithm, which cannot reconstruct the high frequency components for compressed images. The experimental results show that the image quality is improved dramatically. For images in the training set, the improvement of PSNR is about 3 dB. For images, which are outside the training set, the improvement of PSNR is about 0.57 dB, which is comparable to the existing method.
Keywords
Computer Science
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