BoostNet: A Structured Deep Recursive Network to Boost Image Deblocking

To alleviate the conflict between bit reduction and quality preservation, image deblocking as a post-processing strategy is an attractive and promising solution without changing existing codec. Traditional image deblocking methods mainly rely on manually-crafted image prior models, whereas recent deep network-based methods are usually designed in an inexplicable manner. To combine the merits of both categories of deblocking methods, in this paper, we start from formulating image deblocking as an optimization problem, inspired by which we then design a general and interpretable deep structured network for boosting image deblocking, dubbed BoostNet. Each module of BoostNet correlates to one iteration of the optimization step. In addition, the weights across all modules in BoostNet are shared so that the learnable parameters of BoostNet are tremendously reduced. Furthermore, the quantization matrix, which is not considered in previous network-based methods, is incorporated into our BoostNet as an input. Therefore, one single BoostNet can deal well with various quantization strengths. Experiments demonstrate that our proposed structured deep recursive network BoostNet can produce state-of-the-art deblocking results, while maintaining fast computational speed.