Image Compression Techniques: A Comparative Study
1 Lecturer, Department of Electronics and Communication Engineering, Government Polytechnic, Hiriyur- 577599, Karnataka, India.
2 Assistant Professor, Department of Computer Science and Engineering, SIET, Tumkur 572106, Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2020, 06(03), 323-333
Article DOI: 10.30574/wjarr.2020.6.3.0167
Publication history:
Received on 08 June 2020; revised on 17 June 2020; accepted on 23 June 2020
Abstract:
Image compression has become an essential technology in the digital era, enabling efficient storage and transmission of visual data across networks and devices. This paper presents a comprehensive comparative study of various image compression techniques, examining both lossless and lossy compression methods. We analyze traditional techniques such as JPEG and PNG alongside modern approaches including wavelet-based compression and fractal compression. The study evaluates these techniques based on compression ratio, image quality metrics including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), computational complexity, and application suitability. Our findings reveal that while lossy techniques achieve superior compression ratios, lossless methods maintain perfect reconstruction quality at the cost of larger file sizes. The choice of compression technique depends significantly on the specific application requirements, with medical imaging demanding lossless compression while web applications often prioritize smaller file sizes. This research contributes to the understanding of compression technique selection and provides guidelines for practitioners in various domains. The comparative analysis includes experimental results on standard test images, demonstrating the trade-offs between compression efficiency and image fidelity.
Keywords:
Image Compression; Lossy Compression; Lossless Compression; JPEG; JPEG 2000; JPEG-LS; PNG; Rate–Distortion Analysis; PSNR; Bitrate; Compression Ratio; Image Quality Assessment; Digital Image Processing
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Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0