Multi-domain residual encoder–decoder networks for generalized compression artifact reduction

A fundamental requirement for designing compression artifact reduction techniques is to restore the artifact free image from its compressed version regardless of the compression level. Most existing algorithms require the prior knowledge of JPEG encoding parameters to operate effectively. Although there are works that attempt to train universal models to deal with different compression levels, some JPEG quality factors (QF) are still missing. To overcome these potential limitations, in this paper, we present a generalized JPEG-compression artifact reduction framework that relies on improved QF estimator and rectified networks to take into account all possible QF values. Our method, called a generalized compression artifact reducer (G-CAR), first predicts QF by analyzing luminance patches with high activity. Then, based on the estimated QF, images are adaptively restored by the cascaded residual encoder–decoder networks learned in multiple domains. Results tested on six benchmark datasets demonstrate the effectiveness of our proposed model.