Analysis of Adaptive, Entropy and Wavelet based Coding Techniques- A Survey - Subramanyan, N.; Arunesh, K. (International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (Online), 2022)
Nowadays, communication is becoming an important part of everyday human activity. High-quality digital devices are available to everyone, and usage of high-resolution images and videos is increasing in the communication process. Sending plenty of data, especially in the form of photos, audio, and video, over the digital medium is a big challenge because of constraints with bandwidth and volume of storage, and it is costly and time absorbing. Due to the advancement in technology, every field is using computer technology to get more productivity or better results at less cost in less time. For example, when a hospital is taken, every day hundreds of patients' data comes in the form of images and videos, which require large amounts of resources, i.e., storage or network bandwidth for fast processing of accessing and storage. Fast accessing, processing, and storage with the available resources is possible only when compression is used. Image compression techniques work by removing the redundant information from the digital image without diminishing the quality and making the digital image to be represented with a less number of bits which allows easy access and storage without the need for additional resources. In the last three decades, considerable progress has been achieved in the domain of image compression; researchers have presented many algorithms and standards of image compression. However, using new advanced techniques such as machine learning, deep learning, fuzzy-based, and many more, new compression techniques which are dynamic and powerful are being developed. This survey presents a broad overview of existing image coding techniques based on adaptive coding, entropy, and wavelets. A taxonomy of techniques is presented to categorize the methods into different categories. A comparative study of the various compression methods is presented. Parameters to evaluate the performance of the compression algorithm are presented. Finally, the major challenges which are identified during the current research are discussed, and the prospects of future scope are brought forward.