Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution (2212.14046v1)

Abstract: Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges are remained to effectively extract and transmit high-quality textures from high-degraded low-quality sequences, such as blur, additive noises, and compression artifacts. In this work, a novel Frequency-Transformer (FTVSR) is proposed for handling low-quality videos that carry out self-attention in a combined space-time-frequency domain. First, video frames are split into patches and each patch is transformed into spectral maps in which each channel represents a frequency band. It permits a fine-grained self-attention on each frequency band, so that real visual texture can be distinguished from artifacts. Second, a novel dual frequency attention (DFA) mechanism is proposed to capture the global frequency relations and local frequency relations, which can handle different complicated degradation processes in real-world scenarios. Third, we explore different self-attention schemes for video processing in the frequency domain and discover that a ``divided attention'' which conducts a joint space-frequency attention before applying temporal-frequency attention, leads to the best video enhancement quality. Extensive experiments on three widely-used VSR datasets show that FTVSR outperforms state-of-the-art methods on different low-quality videos with clear visual margins. Code and pre-trained models are available at https://github.com/researchmm/FTVSR.

• The paper presents a novel FTVSR model that leverages DCT-based tokenization and dual frequency attention for improved low-quality video super-resolution.
• It introduces a divided attention mechanism that sequentially enhances spatial-frequency and temporal-frequency interactions to recover textures effectively.
• Experimental results demonstrate up to a 0.3 dB PSNR gain on benchmarks like REDS4 and Vid4, proving robust performance against compression and degradation.

Analyzing the Frequency-Transformer Approach to Low-Quality Video Super-Resolution

The paper "Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution" presents a novel framework aimed at addressing the inherent challenges in modern Video Super-Resolution (VSR) tasks, particularly those arising from low-quality or heavily degraded videos. The authors propose the Frequency-Transformer VSR (FTVSR) model, which operates within a joint space-time-frequency domain, departing from the traditional pixel domain to offer improved handling of various degradation processes such as blur, noise, and compression artifacts.

Key Contributions

The main contributions of this work can be categorized into several innovative advancements in VSR technology:

1. Frequency-Based Tokenization: The proposed method employs Discrete Cosine Transform (DCT) to convert video frames into frequency domain representations. This conversion helps in preserving high-frequency visual information while distinguishing real textures from artifacts. Each frame is split into frequency bands, forming tokens that undergo frequency attention mechanisms.
2. Frequency Attention Mechanisms: A core component is the Dual Frequency Attention (DFA) mechanism, specifically designed to handle both global and local frequency relations. This dual approach caters to diverse degradation patterns, such as the local nature of compression artifacts versus globally acting blur or noise.
3. Joint Space-Time-Frequency Attention: The paper investigates various self-attention schemes, concluding that a "divided attention" method—conducting space-frequency attention followed by temporal-frequency attention—yields the highest enhancement quality in video sequences. This approach leverages the spatial and temporal dimensions to efficiently capture dynamic texture changes across frames.
4. Comprehensive Evaluation: The FTVSR model is put through rigorous testing on multiple VSR datasets, demonstrating significant improvement over state-of-the-art methods with clear visual margins. Notably, it manages to handle various compression algorithms and real-world VSR scenarios effectively.

Numerical Results

The paper provides extensive quantitative results, showcasing the FTVSR's ability to surpass existing methods significantly. On the REDS4 and Vid4 datasets, which are standard benchmarks in VSR conventions, FTVSR outperforms competitive models such as BasicVSR and IconVSR, achieving PSNR improvements of up to 0.3 dB. For scenarios involving compressed video input, FTVSR shows even more remarkable gains, evidencing robust capabilities against differing compression standards and real-world degradations.

Implications and Future Work

FTVSR proposes an alternate direction for handling video super-resolution tasks, particularly when dealing with low-quality input. By operating in the frequency domain, this model demonstrates potential not only in texture recovery but also in reducing the effects of various degradation types by utilizing DCT-based transformations and attention mechanisms. The implications for both theoretical development in VSR methods and practical applications in domains like video streaming and content delivery are substantial.

Given these promising results, future developments could explore deeper integration of frequency-based methodologies across different VSR contexts and even expand towards integrating more complex degradation models or leveraging other frequency transformations beyond DCT. Moreover, applying these principles to emerging AI applications, such as temporal video analysis or automatic enhancement for consumer video equipment, could further enrich the field.

In summation, this paper’s contribution to the video super-resolution domain is noteworthy, offering a robust alternative to traditional spatial-only VSR approaches by effectively learning and utilizing frequency dependencies across both spatial and temporal domains.