Existing real-world video super-resolution (VSR) methods focus on designing a general degradation pipeline for open-domain videos while ignoring data intrinsic characteristics which strongly limit their performance when applying to some specific domains (eg., animation videos). In this paper, we thoroughly explore the characteristics of animation videos and leverage the rich priors in real-world animation data for a more practical animation VSR model. In particular, we propose a multi-scale Vector-Quantized Degradation model for animation video Super-Resolution (VQD-SR) to decompose the local details from global structures and transfer the degradation priors in real-world animation videos to a learned vector-quantized codebook for degradation modeling. A rich-content Real Animation Low-quality (RAL) video dataset is collected for extracting the priors. We further propose a data enhancement strategy for high-resolution (HR) training videos based on our observation that existing HR videos are mostly collected from the Web which contains conspicuous compression artifacts. The proposed strategy is valid to lift the upper bound of animation VSR performance, regardless of the specific VSR model. Experimental results demonstrate the superiority of the proposed VQD-SR over state-of-the-art methods, through extensive quantitative and qualitative evaluations of the latest animation video super-resolution benchmark. The code and pre-trained models can be downloaded at https://github.com/researchmm/VQD-SR.

翻译：现有的真实世界视频超分辨率（VSR）方法主要关注为开放域视频设计通用退化流水线，却忽略了数据内在特性，这严重限制了它们在特定领域（如动画视频）中的性能。本文深入探索动画视频的特性，并利用真实动画数据中的丰富先验信息，构建更实用的动画VSR模型。具体而言，我们提出一种多尺度矢量量化退化模型（VQD-SR），用于动画视频超分辨率，将局部细节从全局结构中分离，并将真实动画视频中的退化先验迁移至学习到的矢量量化码本，从而实现退化建模。我们收集了包含丰富内容的大规模真实动画低质量（RAL）视频数据集以提取先验信息。此外，基于当前高分辨率（HR）训练视频多源自网络且存在明显压缩伪影的观察，我们提出一种数据增强策略。该策略无需依赖特定VSR模型即可有效提升动画VSR性能的上限。基于最新动画视频超分辨率基准的定性与定量评估表明，所提出的VQD-SR方法在性能上显著优于现有最先进方法。代码与预训练模型已开源至 https://github.com/researchmm/VQD-SR。