PERFORMANCE ENHANCEMENT IN LOSSY IMAGE COMPRESSION

Abstract

The recent development in the digital electronics and computer engineering has resulted in generation of large amount of data in the digital form. High resolution images are required in many fields such as remote sensing, criminal investigation, medical imaging etc. This motivates the need of compression of the size of the data. Though the cost of storage is rapidly dropping, compression still remains as a challenging issue due to the growing number of multimedia based online applications. newlineThe main goal of the proposed work 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 method, where the main goal is to obtain good quality of decompressed images even at very low bit rates. Due to the wide usage of digital information, image compression becomes more important in the areas like image storage, transmission and processing. A digital image is mainly composed by: edges, edge associated details and textures, and this three parts are very important in reconstruction of an image. So in image compression, it is very important to preserve this information to get a good quality of reconstructed image. A common characteristic of most of images is that the neighboring pixels are correlated. Therefore most important task is to find a less correlated representation of image. The fundamental components of compression are reduction of redundancy and irrelevancy. Redundancy reduction aims at removing duplication from the image. Irrelevancy reduction omits parts of the signal that will not be noticed by the signal receiver namely the human. newline In this proposed work Simulation is carried out using MATLAB software and the performance validations are done on basis of analysis of parameters like Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index Measurement (SSIM).This thesis presents a Lossy image co