Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution (2310.17294v3)

Abstract: The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual quality of videos, by simultaneously performing video frame interpolation (VFI) and video super-resolution (VSR). However, facing the challenge of the additional temporal dimension and scale inconsistency, most existing STVSR methods are complex and inflexible in dynamically modeling different motion amplitudes. In this work, we find that choosing an appropriate processing scale achieves remarkable benefits in flow-based feature propagation. We propose a novel Scale-Adaptive Feature Aggregation (SAFA) network that adaptively selects sub-networks with different processing scales for individual samples. Experiments on four public STVSR benchmarks demonstrate that SAFA achieves state-of-the-art performance. Our SAFA network outperforms recent state-of-the-art methods such as TMNet and VideoINR by an average improvement of over 0.5dB on PSNR, while requiring less than half the number of parameters and only 1/3 computational costs.

• The paper presents the SAFA network which adaptively selects sub-networks for optimal motion estimation and enhanced spatial-temporal feature fusion.
• It employs a novel SAFE block to iteratively refine flow estimates, achieving over 0.5 dB PSNR improvement against state-of-the-art methods with reduced computation.
• SAFA demonstrates both theoretical insights and practical benefits by balancing performance with efficiency, making it viable for real-world video processing applications.

Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution

This paper presents a novel approach to the Space-Time Video Super-Resolution (STVSR) problem, emphasizing the advancement of video enhancement techniques by addressing both video frame interpolation (VFI) and video super-resolution (VSR). The proposed method, termed Scale-Adaptive Feature Aggregation (SAFA), aims to optimize the performance of existing models which often suffer from high complexity and lack of adaptability in processing the additional temporal dimension inherent in videos.

Key Contributions and Methodology

The central contribution of the paper is the introduction of the Scale-Adaptive Feature Aggregation (SAFA) network. This network is designed to dynamically adjust to varying motion scales by selecting the most appropriate sub-networks for individual video samples. The core of this adaptive mechanism is the Scale-Adaptive Flow Estimation (SAFE) block, which iteratively refines motion estimates using a scale selection strategy informed by a dynamic routing technique.

1. Temporal and Spatial Aggregation: SAFA efficiently combines temporal and spatial information by leveraging a motion estimation process that adapts its operational scale. This is achieved through the SAFE block that handles different motion amplitudes by selecting from multiple processing scales, thus increasing flow estimation accuracy and reducing computational load.
2. Adaptive Scale Selection: The method incorporates a data-dependent scale selector which utilizes a Bernoulli distribution to choose the optimal scale for flow estimation. This ensures that computational resources are focused on the most relevant scale for a given video, with shared parameters across scales to maintain efficiency.
3. Iterative Refinement: Building upon techniques like those used in RAFT for optical flow, SAFA employs a recurrent structure for iteratively updating motion information, providing a robust foundation for video super-resolution.

Experimental Results

The SAFA network was evaluated on several public STVSR benchmarks, including Vid4, GoPro, and Adobe240 datasets. Compared to recent state-of-the-art methods such as TMNet and VideoINR, SAFA exhibited superior performance, surpassing these methods by over 0.5 dB in PSNR on average. Notably, SAFA achieves this with less than half the number of parameters and only one-third the computational cost of its counterparts.

These results confirm the efficacy of the proposed scale-adaptive approach, highlighting its potential to significantly improve both the quality and efficiency of STVSR tasks.

Theoretical and Practical Implications

The proposed SAFA network suggests several implications for both theoretical exploration and practical application:

• Theoretical Insights: The approach challenges existing paradigms by demonstrating the efficacy of adaptive scale selection in motion estimation, suggesting new avenues for integrating scale adaptability into other video processing tasks.
• Practical Applications: By achieving state-of-the-art performance with reduced computational overhead, SAFA presents a viable solution for real-world applications requiring efficient video processing, especially in resource-constrained environments.

Future Directions

The success of SAFA in optimizing flow-based video enhancement tasks opens up multiple directions for future research:

• Real-World Adaptations: Extending the SAFA framework to real-world video data, which may include more complex and less predictable motion patterns, could further demonstrate its robustness and adaptability.
• Integration with Advanced Architectures: Exploring the integration of SAFA with transformer-based architectures or other deep learning paradigms might offer even greater improvements in video processing quality and efficiency.
• Broader Applications: The principles underlying SAFA’s adaptive scale mechanism could potentially be applied to a wider range of temporal data processing applications beyond video super-resolution.

In summary, the Scale-Adaptive Feature Aggregation network represents a significant step forward in video super-resolution research, offering a well-justified framework that effectively balances enhancement quality with computational efficiency.