Learning to encode user-generated short videos with lower bitrate and the same perceptual quality

Shengbin Meng, Yang Li, Yiting Liao, Junlin Li, Shiqi Wang

Abstract: On a platform of user-generated content (UGC), the uploaded videos need to be encoded again before distribution. For this specific encoding scenario, we propose a novel dataset and a corresponding learning-based scheme that is able to achieve significant bitrate saving without decreasing perceptual quality. In the dataset, each video's label indicates whether it can be encoded with a much lower bitrate while still keeps the same perceptual quality. Models trained on this dataset can then be used to classify the input video and adjust its final encoding parameters accordingly. With enough classification accuracy, more than 20% average bitrate saving can be obtained through the proposed scheme. The dataset will be further expanded to facilitate the study on this problem.