Review Paper on Lossy Image Compression using Discrete Wavelet Transform and Coding Technique

Abstract

Lossy image compression plays a crucial role in reducing storage requirements and transmission bandwidth while maintaining acceptable visual quality. Among various compression techniques, the Discrete Wavelet Transform (DWT) has emerged as a powerful tool due to its ability to provide multi-resolution representation and superior energy compaction. This review paper presents a comprehensive analysis of lossy image compression methods based on DWT combined with advanced coding techniques. The study explores the fundamental principles of wavelet decomposition, including sub-band coding and hierarchical representation of image data. It further examines prominent coding approaches such as Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), and Embedded Block Coding with Optimized Truncation (EBCOT), highlighting their efficiency in achieving high compression ratios with minimal perceptual loss. Additionally, the paper compares the performance of these techniques using key evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and compression ratio. Recent advancements integrating machine learning and hybrid optimization strategies with DWT-based compression are also discussed to demonstrate improvements in reconstruction quality and computational efficiency. The review identifies challenges such as artifact reduction, edge preservation, and real-time implementation constraints, and outlines future research directions in adaptive wavelet selection and intelligent coding frameworks. Overall, this paper provides valuable insights into the evolution and effectiveness of DWT-based lossy image compression techniques for modern multimedia applications.