提出频域感知方法，提升压缩视频超分辨率质量。
FCVSR: A Frequency-aware Method for Compressed Video Super-Resolution
- 设计频域感知网络，分层处理不同频率子带。
- 在三个数据集上实现最高0.14dB PSNR提升。
- 适合需要高质量视频重建的科研与工业应用。
压缩视频超分辨率（CVSR）旨在从低分辨率压缩视频中生成高分辨率（HR）视频。近年来，部分方法尝试利用频率域中的时空信息，展现出良好性能。然而，这些方法未能在空间上区分不同频率子带，也未捕捉时间频率动态，可能导致次优结果。本文提出一种基于深度频率的压缩视频超分辨率模型（FCVSR），包含运动引导自适应对齐（MGAA）网络和多频特征精炼（MFFR）模块，并设计频域感知对比损失以更好重建细节。该模型在三个公开压缩视频超分辨率数据集上评估，性能优于现有方法，最高达0.14dB PSNR提升，同时保持较低复杂度。
原文摘要 · Abstract (English)
Compressed video super-resolution (SR) aims to generate high-resolution (HR) videos from the corresponding low-resolution (LR) compressed videos. Recently, some compressed video SR methods attempt to exploit the spatio-temporal information in the frequency domain, showing great promise in super-resolution performance. However, these methods do not differentiate various frequency subbands spatially or capture the temporal frequency dynamics, potentially leading to suboptimal results. In this paper, we propose a deep frequency-based compressed video SR model (FCVSR) consisting of a motion-guided adaptive alignment (MGAA) network and a multi-frequency feature refinement (MFFR) module. Additionally, a frequency-aware contrastive loss is proposed for training FCVSR, in order to reconstruct finer spatial details. The proposed model has been evaluated on three public compressed video super-resolution datasets, with results demonstrating its effectiveness when compared to existing works in terms of super-resolution performance (up to a 0.14dB gain in PSNR over the second-best model) and complexity.
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