<![CDATA[In the modern digital era, managing image file sizes is crucial, particularly for devices with limited storage capacity. Images typically have large file sizes due to the extensive amount of color and detail information they contain, which can complicate storage and transmission. To address this issue, data compression techniques are essential for reducing file sizes without significantly sacrificing image quality, especially for images captured by DSLR cameras. This study compares two compression algorithms, Goldbach Codes and Shanon-Fano, to evaluate their effectiveness in compressing *.JPG images. Analysis using Measurement of Performance Expressional (MPE) reveals that Goldbach Codes has a higher priority decision value (7.976) compared to Shanon-Fano (7.868). This indicates that Goldbach Codes is more efficient in image compression, providing a better compression ratio and smaller space savings. In other words, Goldbach Codes proves to be superior in reducing image file sizes with greater speed and effectiveness compared to Shanon-Fano. These findings are significant for applications requiring high-quality image compression in a more efficient and space-saving format.]]>
Copyrights © 2024
