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。

基于分层频率上采样和优化的压缩视频质量提升

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

Low latency rates are crucial for online video-based applications, such as
video conferencing and cloud gaming, which make improving video quality in
online scenarios increasingly important. However, existing quality enhancement
methods are limited by slow inference speed and the requirement for temporal
information contained in future frames, making it challenging to deploy them
directly in online tasks. In this paper, we propose a novel method, STLVQE,
specifically designed to address the rarely studied online video quality
enhancement (Online-VQE) problem. Our STLVQE designs a new VQE framework which
contains a Module-Agnostic Feature Extractor that greatly reduces the redundant
computations and redesign the propagation, alignment, and enhancement module of
the network. A Spatial-Temporal Look-up Tables (STL) is proposed, which
extracts spatial-temporal information in videos while saving substantial
inference time. To the best of our knowledge, we are the first to exploit the
LUT structure to extract temporal information in video tasks. Extensive
experiments on the MFQE 2.0 dataset demonstrate that our STLVQE achieves a
satisfactory performance-speed trade-off.

本文提出了一种新的方法 STLVQE，旨在解决在线视频质量增强问题。我们的 STLVQE 设计了一个新的 VQE 框架，其中包含一个模块不可知特征提取器，大大减少了冗余计算，并重新设计了网络的传播、对齐和增强模块。我们提出了一种空间 - 时域查找表（STL），它在视频中提取了空间 - 时域信息，同时节省了大量推理时间。在 MFQE 2.0 数据集上进行的广泛实验证明，我们的 STLVQE 在性能和速度上取得了令人满意的平衡。

空时查询表进行在线视频质量增强

Online Video Quality Enhancement with Spatial-Temporal Look-up Tables

The latest High Efficiency Video Coding (HEVC) standard has been increasingly
applied to generate video streams over the Internet. However, HEVC compressed
videos may incur severe quality degradation, particularly at low bit-rates.
Thus, it is necessary to enhance the visual quality of HEVC videos at the
decoder side. To this end, this paper proposes a Quality Enhancement
Convolutional Neural Network (QE-CNN) method that does not require any
modification of the encoder to achieve quality enhancement for HEVC. In
particular, our QE-CNN method learns QE-CNN-I and QE-CNN-P models to reduce the
distortion of HEVC I and P frames, respectively. The proposed method differs
from the existing CNN-based quality enhancement approaches, which only handle
intra-coding distortion and are thus not suitable for P frames. Our
experimental results validate that our QE-CNN method is effective in enhancing
quality for both I and P frames of HEVC videos. To apply our QE-CNN method in
time-constrained scenarios, we further propose a Time-constrained Quality
Enhancement Optimization (TQEO) scheme. Our TQEO scheme controls the
computational time of QE-CNN to meet a target, meanwhile maximizing the quality
enhancement. Next, the experimental results demonstrate the effectiveness of
our TQEO scheme from the aspects of time control accuracy and quality
enhancement under different time constraints. Finally, we design a prototype to
implement our TQEO scheme in a real-time scenario.

本研究提出了一种 QE-CNN 方法来增强 HEVC 视频的质量，该方法通过学习 QE-CNN-I 和 QE-CNN-P 模型分别降低 I 和 P 帧的失真，同时进一步提出了 TQEO 方案来控制 QE-CNN 的计算时间以满足时限要求并最大化质量增强效果。