Impact of Compression Algorithms on Filtered Images and Images with Varying Detail Levels

Abstract

In the context of ever-increasing image resolution, efficiently storing and transmitting digital images has become a crucial challenge. This study evaluates the performance of widely-used compression algorithms—JPEG, JPEG 2000, EZW, and SPIHT—focusing on their application to pre-processed images using Laplacian, Prewitt, and Sobel operators. The research reveals how these operators can enhance image features and improve the overall compression process. A detailed comparative analysis of image quality, based on metrics such as mean square error (MSE), signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR), demonstrates the strengths and weaknesses of each compression method across varying bit rates and image detail levels. The results offer valuable insights into optimizing image compression for real-world applications, especially in industries relying on high-fidelity image processing.