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 (e.g. 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.

翻译：现有真实世界视频超分辨率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征，这严重限制了其在特定领域（如动画视频）中的性能。本文深入探索动画视频的特征，利用真实动画数据中的丰富先验信息，构建更实用的动画视频超分辨率模型。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的矢量量化码本中进行退化建模。同时收集了内容丰富的高质量真实动画低分辨率视频数据集以提取先验。此外，基于观察到现有高分辨率训练视频多来源于网络且包含显著压缩伪影，我们提出针对高分辨率训练视频的数据增强策略。该策略能有效提升动画视频超分辨率性能上限，且不依赖于具体超分辨率模型。通过在最新动画视频超分辨率基准上进行的大量定量与定性评估，实验结果表明所提出的VQD-SR方法优于现有最先进方法。