VECTOR QUANTIZATION AND LZW BASED LOSSY IMAGE COMPRESSION

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Published: Feb 20, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i1.5037
Keywords:
Discrete wavelet transform (DWT), Vector quantization (VQ), Peak signal to noise ratio (PSNR), Mean square error (MSE).

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PATEL AMRUTBHAI PATEL
U.V.PATEL COLLEGE OF ENGINEERING, GANPAT UNIVERSITY
Dr. Vijay K. Patel
Prof. Yogesh B. Patel

Abstract

This research paper presents a combined approach to Lossy image compression algorithm, based on wavelet transform, Global thresholding, Vector quantization and Source coding like Huffman coding and LZW coding. In this image compression algorithm, discrete wavelet transform (DWT) is applied on input image, which decomposes the input image into a sequence of wavelet coefficients. Global thresholding is used to modify the wavelet coefficients image. Resultant coefficients after thresholding are quantized using vector quantization technique and later, VQ indices are coded using LZW coding to increase the compression ratio. The main objective of the present study is to obtain better quality of decompressed images even at very low bit rates and to reduce the size of the data as well as processing and transmission time. Considerable efforts have been made to design image compression methods. Experimental results are measured in terms of Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index Measurement (SSIM) for image quality and its performance has been compared to the already existing methods. The proposed method of lossy image compression shows better image quality in terms of PSNR at the same compression ratio as compared to other image compression techniques.

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Vol. 9 No. 1 (2018): January-February 2018
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