Lossy image compression is ubiquitously used for

storage and transmission at lower rates. Among the existing

lossy image compression methods, the JPEG standard is the most widely used technique in the multimedia world. Over the years, numerous methods have been proposed to suppress the compression artifacts introduced in JPEG-compressed images. However, all current learning-based methods include deep convolutional neural networks (CNNs) that are manually-designed by researchers. The network design process requires extensive computational resources and expertise. Focusing on this issue, we investigate evolutionary search for finding the optimal residual block based architecture for artifact removal. We first define a residual network structure and its corresponding genotype representation used in the search. Then, we provide details

of the evolutionary algorithm and the multi-objective function

used to find the optimal residual block architecture. Finally, we present experimental results to indicate the effectiveness of our approach and compare performance with existing artifact removal networks. The proposed approach is scalable and portable to numerous low-level vision tasks.