通过测试时自适应提升视频超分与质量评估的泛化能力
Towards Adaptive Super-Resolution and Quality Assessment via Test-Time Adaptation
- 测试时自适应框架动态调整模型，无需重训练
- 在多个数据集上显著提升文本清晰度与视觉感知质量
- 适合实际场景中未知退化的视频增强任务
本文研究真实环境下自适应视频超分辨率与感知质量建模。现有视频超分辨率（VSR）方法在异构设备、编码器和网络环境导致的未知退化下泛化能力差。我们提出测试时自适应（TTA）统一范式，无需重训练或高质量监督即可提升鲁棒性与感知质量。具体包括：1）基于TTA的无参考视频质量评估（VQA）框架，通过自适应质量预测为未知退化下的VSR提供感知引导；2）基于Transformer的屏幕内容超分辨率架构，有效保持文本清晰度与结构保真度；3）区域感知的TTA策略，可选择性优化文本与非文本区域，无需高分辨率真值。跨多样基准测试结果表明，感知质量与可读性持续提升。同时，本文也展望了面向未见领域的全自适应视频增强系统。
原文摘要 · Abstract (English)
This paper presents doctoral research on adaptive video super-resolution and perceptual quality modeling under real-world conditions. Existing video super-resolution (VSR) methods struggle to generalize under unknown degradations arising from heterogeneous devices, codecs, and network environments. We address this challenge through test-time adaptation (TTA), a unified paradigm that improves robustness and perceptual quality without retraining or high-quality supervision. Specifically, we: 1) propose a TTA-based framework for no-reference video quality assessment (VQA), where adapted quality predictions provide perceptual guidance for VSR under unseen distortions; 2) develop a transformer-based architecture for screen-content super-resolution that preserves text clarity and structural fidelity; and 3) introduce a region-aware TTA strategy that selectively refines text and non-text regions without requiring high-resolution ground truth. Experimental results across diverse benchmarks demonstrate consistent improvements in perceptual quality and readability. We also outline ongoing work toward fully adaptive video enhancement systems capable of generalizing across unseen domains.
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