Video Quality Assessment

Video quality assessment (VQA) aims to objectively measure how well a video is perceived, crucial for optimizing video compression, generation, and enhancement. Current research heavily focuses on developing robust no-reference and full-reference VQA models, often employing deep learning architectures like Swin Transformers and Vision Transformers, and incorporating multimodal information (text, visual, motion) for improved accuracy, particularly in challenging scenarios like AI-generated content and user-generated content. These advancements are vital for improving user experience in video streaming, enhancing video processing algorithms, and establishing standardized benchmarks for evaluating video quality across diverse platforms and applications.