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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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Citation :
IEEE Style Citation: S. Vimala, P. Uma, S. Senbagam, “Adaptive Vector Quantization for Improved Coding Efficiency”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.3, pp.18-22, 2018.

MLA Style Citation: S. Vimala, P. Uma, S. Senbagam "Adaptive Vector Quantization for Improved Coding Efficiency." International Journal of Scientific Research in Network Security and Communication 6.3 (2018): 18-22.

APA Style Citation: S. Vimala, P. Uma, S. Senbagam, (2018). Adaptive Vector Quantization for Improved Coding Efficiency. International Journal of Scientific Research in Network Security and Communication, 6(3), 18-22.

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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

References :
[1] Khalid Sayood, Introduction to DataCompression”,3rd Edition.
[2] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, 2nd Edition.
[3] R. Gray, “Vector Quantization”, IEEE ASSP Mag., pp.4-29,1989.
[4] Yoseph Linde, Andres Buzo, Robert M.Gray, “An Algorithm for Vector Quantizatizer Design”, IEEE Transactions on Communications, Vol. COM-28, pp.84-95, Jan 1980.
[5] Dr. H.B. Kekre, Ms. Tanuja K. Sarode, “Vector Quantized Codebook Optimization Using KMeans Algorithm”, IJCSE, Vol 1(1), pp. 283-290, 2009.
[6] Arup Kumar and Anup Sar, “An Efficient Codebook Initialization Approach For LBG Algorithm”, IJCSEA, Vol.1, pp.72-80, Aug 2011.
[7] Ms. Asmita A Bardekar, Mr. P.A. Tijare, “A Review on LBG Algoithm for Image Compression”, IJCSIT, Vol.2(6), pp. 2584-2589, 2011.
[8] Sayan Nag, “Vector Quantization Using the Improved Differential Evolution Algorithm for Image Compression”, 2017.
[9] K.Somasundaram and S.Vimala, “Simple and Fast Ordered Codebook for Vector Quantization”, Proceedings of the National Conferene on Image Processing, Gandhigram Rural Institute (NCIMP), 2010.

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