Year: 2018
Type: article
Abstract: This paper presents Vector quantization and LZW coding based Lossy image compression technique. VQ is a powerful and classical quantization technique for lossy image compression. VQ is an irreversible process and reduces the psycho-visual redundancies. In this technique, first it divides the input image into P × Q blocks. Where P and Q are number of pixels in X and Y dimensions of Blocks and both are power of 2. After this, it takes each pixel value within the block from left to right, top to bottom to construct a vector. This vector calculates the Euclidean distance with all code words in the codebook. VQ indices are coded using LZW coding to increase the compression ratio. After the decompression, quality of retrieved image is evaluated using SSIM. The aim of the present study is to obtain best quality decompressed images at very low bit rates and higher compression ratio, which in turn can save the storage as well as transmission bandwidth over the internet. 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 assessment.
Author: Dr Amrutbhai N. Patel
Institution: Ganpat University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2808301431