Comparative analysis of image coding methods A State-of-the-Art Survey.

The need for image/video storage and transmission is growing dramatically in the current era of multimedia for purposes such as imaging, online meetings, and video streaming, etc. As a result of technological advancements, there has been a steady increase in communication channel network capacity. As a result of new advanced technology and increased capacity of existing ones, bandwidth requirement is increasing exponentially.Many of the current initiatives in the field of data compression are described by it. The objective of these endeavors is to propose new methods for encoding information sources like audio, images, and video in a manner that reduces the number of bits needed to represent the source content without noticeably compromising the quality. There is a necessity of the new methods that works by reducing the source data without significantly limiting the quality of the source. This is the main intension of these works. In the recent, there has been a significant increase in image compression research, which corresponds with a noteworthy rise in the generation of digital data in the form of images. The objective is to preserve the vital information contained in an image while representing it in the fewest possible bits. The three major types of information that images provide are irrelevant, redundant, and useful. Within the field of image information, there is a kind of data called deterministic redundant information that can be recovered with no loss by utilizing other data that is contained in the image. On the other hand, data that is excessively detailed and goes beyond the limits of perceptual significance is categorized as irrelevant data. Conversely, the part of the material that is neither redundant nor superfluous is the valuable information. The human visual system's limitations and capabilities provide the basis for how the ordinary person interprets decompressed images. This survey provides a comprehensive overview of current lossless and lossy image coding techniques. The methods are divided into many groups, and a taxonomy of techniques is shown. The different compression techniques are compared in this study. The compression algorithm performance evaluation parameters are presented. The main issues that were discovered during the current research are then reviewed, and potential directions for the future are presented. [ABSTRACT FROM AUTHOR]