Index compression for vector quantization using principal index-pattern coding algorithm

Liu, Yung-Chih; Lee, Gwo-Her; Liao, Jan‐Ray; Chi, Li-Pin; Taur, J.S.

doi:10.1117/1.jei.23.4.043015

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…To reduce the BRs of VQ, some image coding methods, [18][19][20][21] which aim to losslessly compress the IT of VQcompressed image, have been proposed. To reduce the BRs of VQ, some image coding methods, [18][19][20][21] which aim to losslessly compress the IT of VQcompressed image, have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Refined codebook for grayscale image coding based on vector quantization

Chen

Tsai

2015

Opt. Eng

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Vector quantization (VQ) is a commonly used technique for image compression. Typically, the common codebooks (CCBs) that are designed by using multiple training images are used in VQ. The CCBs are stored in the public websites such that their storage cost can be omitted. In addition to the CCBs, the private codebooks (PCBs) that are designed by using the image to be compressed can be used in VQ. However, calculating the bit rates (BRs) of VQ includes the storage cost of the PCBs. It is observed that some codewords in the CCB are not used in VQ. The codebook refinement process is designed to generate the refined codebook (RCB) based on the CCB of each image. To cut down the BRs, the lossless index coding process and the twostage lossless coding process are employed to encode the index table and the RCB, respectively. Experimental results reveal that the proposed scheme (PS) achieves better image qualities than VQ with the CCBs. In addition, the PS requires less BRs than VQ with the PCBs.Hu, Chen, and Tsai: Refined codebook for grayscale image coding based on vector quantization Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 08/18/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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