COMPARATIVE ANALYSIS OF LOSSY IMAGE COMPRESSION ALGORITHMS

Abstract

The demand for efficient image storage and transmission has driven extensive research into lossy image compression algorithms. This paper presents a comprehensive comparative analysis of three prominent lossy image compression techniques: Discrete Cosine Transform (DCT), Wavelet Transform, and Vector Quantization (VQ). Using a diverse dataset and various evaluation metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Bitrate, and Computational Complexity, we assess their performance in terms of image quality, compression efficiency, and computational demands. Our findings reveal that DCT excels in preserving image quality, closely followed by Wavelet Transform. VQ, while efficient in compression, lags in image quality preservation. Based on the comparative analysis of three key lossy image compression algorithms, it was observed that DCT stands out as the most appropriate technique to consider for applications that prioritize image quality preservation. It offers high Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) scores, indicating superior image fidelity. While it may not be the most computationally efficient, DCT strikes a balance between compression efficiency and image quality.