Comprehensive Review of Image Compression Techniques in Digital Image Processing

Abstract: Digital Image Processing (DIP) has become an essential technology in modern communication, healthcare, multimedia systems, remote sensing, surveillance, and scientific research. The rapid growth of high-resolution image data has increased the demand for efficient image storage and transmission methods. Image compression techniques play a major role in reducing image size by eliminating redundant information while maintaining acceptable image quality. This review paper presents a comprehensive study of different image compression methods used in digital image processing. The paper discusses both lossless and lossy compression techniques, including JPEG, PNG, Huffman Coding, Run Length Encoding (RLE), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), fractal compression, and modern deep learning-based approaches. A comparative analysis of compression ratio and image quality is also presented. Furthermore, recent developments in artificial intelligence and hybrid compression models are reviewed to understand future trends in image compression systems.

Keywords: Digital Image Processing, Image Compression, Lossless Compression, Lossy Compression, DCT, DWT, JPEG, JPEG2000, Deep Learning.