发表机构
Indian Institute of Technology Jammu（贾姆穆印度理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对真实场景下VSR泛化不足的问题，提出基于TTA的无参考VQA框架、Transformer屏幕内容超分辨率架构及区域感知TTA策略，在多基准上实现感知质量与可读性的提升。
AI 中文摘要
本文呈现了针对真实场景下自适应视频超分辨率与感知质量建模的博士研究。现有视频超分辨率（VSR）方法在异构设备、编解码器及网络环境产生的未知降质下泛化能力不足。我们通过测试时适应（TTA）这一统一范式解决该挑战，其无需重新训练或高质量监督即可提升鲁棒性与感知质量。具体而言，我们：1）提出基于TTA的无参考视频质量评估（VQA）框架，其中适配后的质量预测为未见失真下的VSR提供感知指导；2）开发基于Transformer的屏幕内容超分辨率架构，以保留文本清晰度与结构保真度；3）引入区域感知TTA策略，该策略无需高分辨率真值即可选择性优化文本与非文本区域。在多样基准上的实验结果表明，感知质量与可读性均获得持续提升。我们还概述了针对完全自适应视频增强系统的在研工作，该系统可跨未见域泛化。
英文摘要
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.
Comments5 pages, 5 figures, 4 tables, 34th ACM International Conference on Multimedia, Accepted as Doctoral Symposium Track
