<![CDATA[Advances in information technology currently play an important role in the rapid exchange of information. Information in the form of images requires adequate storage media due to the large size of images, thus requiring an efficient method, namely image compression. This study aimed to evaluate the performance of the Shannon-Fano and Huffman Coding algorithms in terms of compression ratio, processing time, and resistance to Additive White Gaussian Noise (AWGN). The solution in this study was the application and comparison of two compression algorithms to determine the more efficient compression algorithm. The algorithms used were Shannon-Fano and Huffman Coding for the compression of 256×256 and 512×512 BMP images. After the reconstruction process, the images were tested using the AWGN noise effect with noise variance ranging from 0.01 to 0.5 and filtered using a Gaussian filter to assess the image’s resistance to noise. The results showed that the Huffman Coding algorithm produced higher compression ratios of 5.60% and 5.65% compared to Shannon-Fano’s 5.51% and 5.58% for images measuring 256×256 and 512×512, respectively. However, the Huffman Coding algorithm required longer processing time than Shannon-Fano. AWGN noise testing showed that an increase in noise variance reduced image quality, but the application of a Gaussian filter could improve image quality. This study showed that Huffman Coding is more efficient in compression algorithms, while Shannon-Fano is superior in processing time, and the reconstructed image was able to maintain image quality after being affected by noise. The results can be used as a reference in selecting an efficient image compression algorithm to improve transmission channel performance]]>
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