STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution (2501.02976v1)

Abstract: Image diffusion models have been adapted for real-world video super-resolution to tackle over-smoothing issues in GAN-based methods. However, these models struggle to maintain temporal consistency, as they are trained on static images, limiting their ability to capture temporal dynamics effectively. Integrating text-to-video (T2V) models into video super-resolution for improved temporal modeling is straightforward. However, two key challenges remain: artifacts introduced by complex degradations in real-world scenarios, and compromised fidelity due to the strong generative capacity of powerful T2V models (\textit{e.g.}, CogVideoX-5B). To enhance the spatio-temporal quality of restored videos, we introduce\textbf{~\name} (\textbf{S}patial-\textbf{T}emporal \textbf{A}ugmentation with T2V models for \textbf{R}eal-world video super-resolution), a novel approach that leverages T2V models for real-world video super-resolution, achieving realistic spatial details and robust temporal consistency. Specifically, we introduce a Local Information Enhancement Module (LIEM) before the global attention block to enrich local details and mitigate degradation artifacts. Moreover, we propose a Dynamic Frequency (DF) Loss to reinforce fidelity, guiding the model to focus on different frequency components across diffusion steps. Extensive experiments demonstrate\textbf{~\name}~outperforms state-of-the-art methods on both synthetic and real-world datasets.

An Overview of the STAR Framework for Real-World Video Super-Resolution

Introduction

The paper presents STAR, a sophisticated framework for enhancing real-world video resolution by leveraging spatial-temporal augmentation strategies combined with powerful text-to-video (T2V) diffusion models. The proposed method effectively addresses key challenges in video super-resolution (VSR), such as temporal consistency and degradation artifact removal. This work elucidates the integration of large-scale pre-trained image diffusion models into a practical VSR setting and introduces novel components to improve the fidelity and clarity of video restoration tasks.

Methodology

The STAR framework harnesses T2V models, typically challenged by temporal dynamics due to their training on static images, and augments them with a Spatial-Temporal Augmentation approach. This is achieved through the implementation of the Local Information Enhancement Module (LIEM) and the Dynamic Frequency (DF) Loss, addressing local detail enrichment and fidelity improvement, respectively.

1. Local Information Enhancement Module (LIEM): This component is strategically inserted before global attention in the T2V framework to heighten the model's sensitivity to local details, which are often crucial in dealing with real-world degradation artifacts. LIEM effectively balances the necessity of capturing fine-grained spatial features with the broader demands of video analytics.
2. Dynamic Frequency Loss (DF Loss): The DF Loss differentiates between high- and low-frequency components, guiding the model to prioritize these components across different diffusion stages. This loss function enhances the fidelity of the restoration by aligning the model’s generative steps with the natural hierarchical structure of video content, further assisting in maintaining the integrity of temporal information.

Evaluation and Results

STAR's capabilities are rigorously tested across several datasets, including synthetic benchmarks (UDM10, REDS30, and OpenVid30) and a real-world dataset (VideoLQ). The evaluation employs various metrics, notably PSNR, SSIM, LPIPS, among others. The results accentuate STAR's competitive performance, demonstrating noteworthy improvements in visual quality over existing state-of-the-art methods. On metrics such as DOVER and flow warping error (E$_{warp}^*$), STAR consistently outperforms alternatives, emphasizing its robustness in achieving temporal consistency without compromising on spatial clarity.

Implications and Future Directions

The integration of T2V diffusion models into real-world VSR sets a new trajectory in leveraging pre-trained models aimed at diverse applications. STAR not only advances the state of video restoration but also hints at the potential of using large-scale diffusion models as foundational architectures for related AI tasks. The use of LIEM and DF Loss illustrates innovative pathways for refining model performance in VSR, hinting at additional avenues where T2V models could be adapted for specialized tasks requiring high fidelity and temporal coherence.

Conclusion

STAR exemplifies an adept confluence of advanced neural modeling techniques and practical augmentation strategies, affirming the utility of T2V models in video super-resolution tasks. The results achieved underscore the prototype’s pragmatic relevance, particularly in contexts burdened by non-trivial degradation conditions. Moving forward, the scope for leveraging increasingly robust T2V models positions STAR as a potential framework not only in VSR but potentially in a broader spectrum of video analytics and enhancement applications. This research lays a solid groundwork for the further exploration of generative models in sophisticated AI ecosystems.