A Hybrid Approach to Blind Video Quality Prediction of User Generated Content

Increased use of social media platforms has resulted in a vast amount of user-generated video content being released to the internet daily. Measuring and monitoring the perceptual quality of these videos is vital for efficient network and storage management. However, these videos do not have a pristine reference, posing challenges for accurate quality monitoring. In this paper, we introduce a hybrid metric to measure the perceptual quality of user-generated video content using both pixel-level and compression-level features. Our experiments on large-scale databases of user-generated content show that the proposed method performs comparably in predicting the perceptual quality when compared with state-of-the-art metrics.