New TTA Framework Enhances Video Super-Resolution and Quality Assessment

This paper introduces a novel test-time adaptation (TTA) framework designed to enhance video super-resolution (VSR) and perceptual quality assessment under diverse and unknown real-world conditions. The research addresses the limitations of existing VSR methods in generalizing across various devices, codecs, and network environments. Key contributions include a TTA-based approach for no-reference video quality assessment that guides VSR, a transformer architecture for screen-content super-resolution focusing on text clarity, and a region-aware TTA strategy for selective refinement without ground truth. AI

IMPACT Introduces novel methods for improving video quality and resolution, potentially impacting media processing and user experience.

RANK_REASON The cluster contains a research paper submitted to arXiv detailing new methods in computer vision. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Ajeet Kumar Verma
• arXiv
• No-Reference Video Quality Assessment Based on Three-Dimensional Convolutional Neural Networks
• Region-Aware Test-Time Adaptation
• Screen-Content Super-Resolution
• Test-Time Adaptation
• Transformer++
• Video Super Resolution

• paper
• model release

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