Video Quality Assessment Model

Video quality assessment (VQA) models aim to automatically predict the perceived quality of videos, a crucial task for improving user experience in various applications. Current research focuses on enhancing model generalization across diverse video types (e.g., user-generated content, high dynamic range video) and addressing specific distortion types (e.g., compression artifacts, temporal inconsistencies), often employing deep learning architectures like convolutional neural networks and transformers, sometimes incorporating vision-language models or human visual system principles. These advancements are significant for optimizing video streaming, compression, and enhancement technologies, as well as for providing more accurate and reliable quality metrics for research and development.