Analyzing the Trade-offs in Lossless Image Compression Techniques:Insights for Computer Science Research

Abstract

This paper aims to provide a comprehensive analysis of the pros and cons of various lossless image compression algorithms for computer scientists, including RLE, Huffman coding, and LZ77. The pros and cons of different compression methods will be examined by various metrics such as space efficiency, space complexity, and time complexity. Each method will be tested upon various image file types, including BMP, TIFF, PPM, JPG, and PNG. The results indicated that Huffman encoding was particularly effective for PPM images, outperforming RLE and LZ77 with notably higher compression ratios. RLE had slightly higher compression ratios in compressing BMP files. TIFF images exhibit lower compressibility compared to BMP and PPM, but with Huffman encoding still demonstrating superior results. However, when lossless compression algorithms are applied to JPG and PNG images, they yield negative outcomes, indicating that JPG and PNG files have limited compressibility due to prior compression.