Index compressed image adaptive vector quantisation

Shanbehzadeh, Jamshid; Ogunbona, Philip

doi:10.1016/0923-5965(95)00062-3

Cited by 2 publications

(1 citation statement)

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“…Two groups ofmethods have been proposed to achieve this goal. The first group is VQ schemes with memory: predictive, finite state, adaptive VQ The second group, which we call it index compressed VQ, is based on basic VQ, and a compression scheme to exploit the correlation of codewords (or indices) 6,7,8.9.10,11 The results of researchers show that the second group of schemes is able to provide better than the first one at bit rates less that 0.3 bits/pixel 6,7,8,9,10,11 Based on the method utilized in inter-block dependency removal, the index compressed VQ schemes can be divided into two groups. The first group, such as Address VQ 6,7and Index Compressed VQ 8,9, uses a lossless, and the second one uses a lossy IOu scheme.…”

Section: Introductionmentioning

confidence: 98%

<title>Image compression based on genealogical relation of the TSVQ indices</title>

1998

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The indices obtained by tree-structured vector quantisation (TSVQ) have an interesting property that enables them to give information about the correlation between two image blocks. Iftwo image blocks are highly correlated, they may have an identical index, or the same ancestors. The existence of high inter-block correlation in natural images results in having neighboring blocks with the same genealogy. This characteristic can be used to compress the indices. This paper introduces a novel method to exploit the genealogical relation between the image block indices obtained from a TSVQ. The performance of this scheme in terms of PSNR versus average rate was compared with some other similar image coders. The results show that this scheme has better compression capability in terms of objective and subjective quality over these schemes at bit rates less than 0.3 bpp.

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