FastCNN: Towards Fast and Accurate Spatiotemporal Network for HEVC Compressed Video Enhancement

Abstract

Deep neural networks have achieved remarkable success in HEVC compressed video quality enhancement. However, most existing multiframe-based methods either deliver unsatisfactory results or consume a significant amount of resources to leverage temporal information of neighboring frames. For the sake of practicality, a thorough investigation of the architecture design of the video quality enhancement network regarding enhancement performance, model parameters, and running speed is essential. In this article, we first propose an efficient alignment module that can quickly and accurately aggregate the spatiotemporal information of neighboring frames. The proposed module estimates deformable offsets progressively in lower-resolution space motivated by the observation of offset correlations between adjacent pixels. Then, the quantization parameter (QP) that represents compression level prior knowledge is utilized to guide aligned feature enhancement. By combining alignment feature distillation with residual feature correction, we obtain an efficient QP attention block. To save the storage space of the network, we design a hash buffer to store QP embedding features. These efficient components allow our network to effectively exploit temporal redundancies and obtain favorable enhancement capability while maintaining a lightweight structure and fast running speed. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art methods over different QPs by up to 0.09 to 0.11 dB, whereas the inference time can be reduced by up to 69%.