Testing the effectiveness of quantization matrices in (image) compression is difficult, because each image is unique and requires individualization techniques to optimize the compression ratio. However, there is usually no time to perform these kinds of techniques each time an image is compressed, and thus more general (but less effective) quantization matrices are often used. Although these matrices are standardized, there is still room for improvement. This paper proposes the usage of a popular machine learning technique, genetic algorithms, to actually perform this improvement. Although there might be some generalization issues, we believe it can be partially overcome if the right training set is chosen.
II. GENETIC ALGORITHMS
III. EVOLUTIONARY COMPRESSION APPROACHES