  This paper shows that it is possible to train large and deep convolutional
neural networks (CNN) for JPEG compression artifacts reduction, and that such
networks can provide significantly better reconstruction quality compared to
previously used smaller networks as well as to any other state-of-the-art
methods. We were able to train networks with 8 layers in a single step and in
relatively short time by combining residual learning, skip architecture, and
symmetric weight initialization. We provide further insights into convolution
networks for JPEG artifact reduction by evaluating three different objectives,
generalization with respect to training dataset size, and generalization with
respect to JPEG quality level.
