The Impact of Parallel Processors on Image Compression: A Review

Abstract

    The rapid growth of digital data on the Internet has led to significant challenges, including high costs for storage and transmission. Image compression is a critical solution to reduce memory requirements and associated expenses. Researchers have focused on improving compression algorithms to enhance performance, speed, and efficiency by leveraging parallelism at the task level. The primary goal of compression is to reduce file sizes for easier storage and transmission while maintaining acceptable image quality. Parallel processors play a vital role in enhancing the performance of image compression algorithms, particularly for processing large datasets and meeting the demands of modern applications. This review examines the impact of parallel processors on improving compression efficiency compared to traditional methods, addressing challenges such as data distribution among processors, image quality, and compression ratios. It explores various techniques, including block-level and multi-layer processing, and evaluates their effects on computational complexity, image quality, and interprocessor communication. By analyzing previous studies, this review provides insights into effective strategies for optimizing image compression using parallel processing, considering factors such as computational efficiency and quality preservation. Future work should focus on developing advanced parallel techniques to further enhance compression performance and address existing limitations.