Vector Quantization Based Image Compression
P.Sivakumar1, S.Ravi2
1P.Sivakumar, Research Scholar, st. peter’s University Chennai, India.
2Dr.S.Ravi, Professor & Head, Dr. M.G.R. University Chennai, India.
Manuscript received on May 01, 2012. | Revised Manuscript received on May 30, 2012. | Manuscript Published on June 10, 2012. | PP: 89-96 | Volume-1 Issue-1, June 2012. | Retrieval Number: A119051112/2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An image compression method combining discrete wavelet transform (DWT) and vector quantization (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate sub bands. These sub bands are then vector quantized. VQ indices are Huffman coded to increase the compression ratio. Lloyd extended scalar quantization technique is used to design memory less vector quantization. A novel iterative error correction scheme is proposed to continuously check the image quality after sending the Huffman coded bit stream of the error codebook indices through the channel so as to improve the peak signal to noise ratio (PSNR) of the reconstructed image. The sub band of the wavelet transformed image is also generated for the error correction scheme using the difference between the original and the reconstructed images in the wavelet domain. The proposed method shows better image quality in terms of PSNR at the same compression ratio as compared to other DWT and VQ based image compression. The proposed method of image compression is to obtain the best possible fidelity for given rate.
Keywords: Vector Quantization, Wavelet Transform, Compression Ratio.