Information Theory Based Image Compression Algorithms

Abstract

In today’s digital era, the exponential growth of multimedia content has created a pressing need for efficient storage and transmission techniques. Among various data types, images occupy a significant portion of storage systems and communication bandwidth. Image compression, therefore, becomes an essential tool to manage this vast amount of visual data effectively. The fundamental goal of image compression is to reduce the number of bits required to represent an image while maintaining acceptable visual quality.

Information theory serves as the backbone of modern compression techniques by offering a mathematical framework to quantify information, redundancy, and optimal encoding limits. Concepts such as entropy, mutual information, and rate-distortion theory provide insights into how efficiently data can be compressed without losing critical information. This paper presents a detailed exploration of information theory-based image compression algorithms, their underlying principles, methodologies, and real-world applications. Furthermore, it highlights recent advancements that integrate deep learning with classical information-theoretic approaches to achieve superior performance.