Reference Video Quality

Reference video quality assessment aims to objectively measure the perceptual quality of videos, often without a pristine reference version, a crucial task given the explosion of user-generated content. Current research heavily utilizes deep learning models, particularly transformer-based architectures and convolutional neural networks, focusing on improving robustness against adversarial attacks and handling diverse distortions common in real-world videos, including those from high dynamic range (HDR) sources. These advancements are vital for enhancing video streaming services, optimizing compression algorithms, and developing more reliable quality control systems across various platforms.