Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement

Video compression artifacts arise due to the quantization operation in the frequency domain. The goal of video quality enhancement is to reduce compression artifacts and reconstruct a visually-pleasant result. In this work, we propose a hierarchical frequency-based upsampling and refining neural network (HFUR) for compressed video quality enhancement. HFUR consists of two modules: implicit frequency upsampling module (ImpFreqUp) and hierarchical and iterative refinement module (HIR). ImpFreqUp exploits DCT-domain prior derived through implicit DCT transform, and accurately reconstructs the DCT-domain loss via a coarse-to-fine transfer. Consequently, HIR is introduced to facilitate cross-collaboration and information compensation between the scales, thus further refine the feature maps and promote the visual quality of the final output. We demonstrate the effectiveness of the proposed modules via ablation experiments and visualized results. Extensive experiments on public benchmarks show that HFUR achieves state-of-the-art performance for both constant bit rate and constant QP modes.

Comprehensive Soccer Video Understanding: Towards Human-comparable Video Understanding System in Constrained Environment

Comprehensive video understanding, a challenging task in computer vision to understand videos like humans, has been explored in ways including object detection and tracking, action classification. However, most works for video understanding mainly focus on isolated aspects of video analysis, yet ignore the inner correlation among those tasks. Sports games videos can serve as a perfect research object with restrictive conditions, while complex and challenging enough to study the core problems in computer vision comprehensively. In this paper, we propose a new soccer video database named SoccerDB with the benchmark of object detection, action recognition, temporal action detection, and highlight detection. We further survey a collection of strong baselines on SoccerDB, which have demonstrated state-of-the-art performance on each independent task in recent years. We believe that the release of SoccerDB will tremendously advance researches of combining different tasks in closed form around the comprehensive video understanding problem. Our dataset and code will be published after the paper accepted.