Image compression based on genetic algorithm optimization

Image compression has attracted a lot of research since the beginning of Internet era and telecommunication. Enhancing image compression quality and ratio was achieved through several approaches such as neural networks and discrete transforms. However, other heuristic and bio-inspired methods such as genetic algorithms are still under experimentation. In this paper, we introduced a new image compression mechanism based on exploiting the relationship between fractional numbers and their corresponding quotient representation. Each sub-image is mapped to a fractional number based on the RGB representation, and then reduced to an efficient quotient. The appeal of using genetic algorithms is explained by the massive search to find a close fraction that is reduced to short quotient. Our method showed a considerable compression ratio when the least significant bits of each byte are altered, hence, the image quality is preserved while achieving high compression ratio.