Adaptive Vector Quantization for Improved Coding Efficiency

S. Vimala¹ , P. Uma² , S. Senbagam³

Section:Research Paper, Product Type: Journal
Vol.6 , Issue.3 , pp.18-22, Jun-2018

Online published on Jun 30, 2018

Copyright © S. Vimala, P. Uma, S. Senbagam . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In this paper, we propose a novel method of improving the initial codebook for Vector Quantization (VQ) to compress still images. VQ is a simple and efficient compression technique which comprises of three phases: 1. Codebook Generation 2. Index Map Generation and 3. Image Reconstruction. Default codebook is generated first and is improved by refining the representative vectors called the code vectors. The codebook optimization technique proposed in this paper improves the quality of reconstructed images to a greater extent where the average bpp is decreased to a value of 0.79 which is a significant improvement. Benchmark images such as Lena, Kush, Cameraman, Barbara are tested with the proposed technique and this technique produces better results.

Key-Words / Index Term :
Image Compression, Vector Quantization, Index Map, bpp, Still Image

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