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.

本研究提出了一种基于频率层次的上采样和优化神经网络（HFUR）来提高压缩视频质量，通过利用 DCT 域导出的先验信息进行无显式转换的频率上采样，并通过分层和迭代的优化提高最终输出的视觉质量。通过消融实验和可视化结果证明了所提出模块的有效性，并在公开基准测试集上进行了广泛实验，表明在恒定比特率和恒定 QP 模式下 HFUR 实现了最先进的性能。