Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution

• Published in: Computer Vision - ECCV 2022 Vol. 13678; pp. 257 - 273
• Main Authors: Qiu, Zhongwei, Yang, Huan, Fu, Jianlong, Fu, Dongmei
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer 2022; Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• Subjects: Compression; Frequency learning; Transformer; VSR
• ISBN: 9783031197963; 3031197968
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-19797-0_15

Compressed video super-resolution (VSR) aims to restore high-resolution frames from compressed low-resolution counterparts. Most recent VSR approaches often enhance an input frame by “borrowing” relevant textures from neighboring video frames. Although some progress has been made, there are grand challenges to effectively extract and transfer high-quality textures from compressed videos where most frames are usually highly degraded. In this paper, we propose a novel Frequency-Transformer for compressed video super-resolution (FTVSR) that conducts self-attention over a joint space-time-frequency domain. First, we divide a video frame into patches, and transform each patch into DCT spectral maps in which each channel represents a frequency band. Such a design enables a fine-grained level self-attention on each frequency band, so that real visual texture can be distinguished from artifacts, and further utilized for video frame restoration. Second, we study different self-attention schemes, and discover that a “divided attention” which conducts a joint space-frequency attention before applying temporal attention on each frequency band, leads to the best video enhancement quality. Experimental results on two widely-used video super-resolution benchmarks show that FTVSR outperforms state-of-the-art approaches on both uncompressed and compressed videos with clear visual margins. Code are available at https://github.com/researchmm/FTVSR.