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.

翻译：视频压缩伪影源于频域中的量化操作。视频质量增强的目标是减少压缩伪影并重建视觉上令人满意的结果。本文提出一种层次化频率上采样与精化神经网络（HFUR），用于压缩视频质量增强。HFUR由两个模块组成：隐式频率上采样模块（ImpFreqUp）和层次化迭代精化模块（HIR）。ImpFreqUp通过隐式DCT变换提取DCT域先验信息，并通过由粗到细的传输精确重建DCT域损失。进而引入HIR以促进多尺度间的交叉协作与信息补偿，从而进一步精化特征图并提升最终输出的视觉质量。通过消融实验与可视化结果证明了所提模块的有效性。在公开基准上的大量实验表明，HFUR在恒定位率模式和恒定QP模式下均达到了最先进的性能。