FCVSR: A Frequency-aware Method for Compressed Video Super-Resolution

• URL: http://arxiv.org/abs/2502.06431v1
• Date: Mon, 10 Feb 2025 13:08:57 GMT
• Title: FCVSR: A Frequency-aware Method for Compressed Video Super-Resolution
• Authors: Qiang Zhu, Fan Zhang, Feiyu Chen, Shuyuan Zhu, David Bull, Bing Zeng,
• Abstract summary: We propose a deep frequency-based compressed video SR model (FCVSR) consisting of a motion-guided adaptive alignment network and a multi-frequency feature refinement module. The proposed model has been evaluated on three compressed video compressed super-resolution datasets.
• Score: 26.35492218473007
• License:
• Abstract: 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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