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- Title
VECTOR QUANTIZATION AND LZW BASED LOSSY IMAGE COMPRESSION.
- Authors
Patel, Amrutbhai N.; Patel, Vijay K.; Patel, Yogesh B.
- 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. Main goal of proposed work is to lower the execution and transmission time with maintaining higher values of CR and PSNR for various images. Proposed algorithm is applied on various images and results are analyzed using performance CR, PSNR, MSE and SSIM for image quality and its performance has been compared with the already existing methods.
- Subjects
VECTOR quantization; IMAGE compression; HUFFMAN codes; DISCRETE wavelet transforms; SIGNAL-to-noise ratio
- Publication
International Journal of Advanced Research in Computer Science, 2018, Vol 9, Issue 1, p253
- ISSN
0976-5697
- Publication type
Article
- DOI
10.26483/ijarcs.v9i1.5037