YOCO: Light-Weight Rate Control Model Learning

The knowledge of the bitrates and coding parameters is the prerequisite for implementing the rate control mechanism. Many previous works have been attempted to avoid the actual coding to obtain these factors by studying their regularity of correlation. However, these works only focus on the studies on the picture level or the coding unit (CU) level rather than on the sequence level. In this paper, we propose the YOCO (You Only Code Once) light-weight rate control model learning scheme, which can achieve the sequence-level rate control by managing the constant rate factor (CRF). It utilizes the unified information extracted from the compressed videos in the bitstream and pixel domains, leveraging the deep learning algorithm to learn the rate control model as these factors' algebraic relevance. For each video sequence, we can allocate the bitrates extremely approaching the target value with coding at the estimated CRF setting. We compare the application of pixel domain information on each rate control model (linear and the quadratic R-CRF models) to validate their effectiveness. The experimental results demonstrate the improvement of accuracy on the bitrate estimation in the different definitions, especially in the high ones.