Content-Adaptive Rate Control Method for User-Generated Content Videos

In recent years, user-generated content (UGC) videos have become the mainstream of internet videos, which are characterized by their rich content, complicated temporal changes and multiple distortions. However, existing rate control (RC) methods do not consider the above unique characteristics, leading to severe bit-rate errors and coding performance degradation. To address these issues, we propose a content-adaptive RC method for UGC videos, where accurate RC coding parameters are derived by our proposed rate-distortion (RD) model derivations for different types of pictures and a novel bit allocation refinement module. Specifically, the RD models of intra pictures are derived by established SVR-based predictors using some features designed for diverse content, such as texture complexity and regularity. Considering the complex temporal variation, single-reference inter pictures are firstly classified into three categories (i.e., low, regular and high correlation) by a SVM-based classifier using correlation-based features. Training data of the classifier are labeled by introducing a series of classification metrics. Then, RD model is derived by established predictors accordingly for each type of inter pictures. In addition, the RD model of multiple-reference inter pictures is derived by using a updated RD model selection based on content similarity. Based on derived RD models, allocated bits are refined to reduce bit waste. Experimental results show that compared with the default RC method in versatile video coding (VVC), our method can effectively save BD-Rate and reduce bit-rate errors for UGC videos. In particular, 1.99% BD-Rate saving and 0.18% bit-rate error reduction can be achieved under the random access (RA) configuration, and 0.45% BD-Rate saving under the low-delay B (LDB) configuration.