| Summary: | Enormous number of images are generated daily in all areas of life, including social media, medical and navigation images. Moreover, the development of smart phones among other specialized media-capturing devices has witnessed great advances during the last decade. Consequently, the storage, transmission, and analysis of images become essential and frequent tasks. Thus, various research efforts tried to address the image compression problem from different computational perspectives. This article presents a novel multilevel lossy compression algorithm for grayscale images, namely <bold>Image-as-Protein</bold> (<italic>IaP</italic>), that is inspired by the translation of <italic>DNA</italic> sequences into <italic>protein</italic> sequences that occurs inside live beings. Because of the high similarity of the resulting textual <italic>protein</italic> sequence, it can be tackled by general text compression techniques with competitive compression ratios. Various qualitative comparisons and quantitative measures such as <italic>BPP</italic>, <italic>SSIM</italic> and <italic>PSNR</italic> have been carried out on multiple grayscale image benchmark datasets. The experimental results showed that the proposed algorithm is promising compared to the famous JPEG lossy image compression standard.
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