LEARNING-BASED VIDEO COMPRESSION WITH CONTINUOUSLY VARIABLE BITRATE CODING

In this paper, we propose a learning-based video compression which can perform continuously variable bitrate coding. The proposed method generates feature transformation parameters through a conditional network according to the input spatial quality map. These parameters are then used to adaptively transform the intermediate features of the encoder, decoder, and spatiotemporal entropy model in the codec, thus enabling variable bitrate coding. Additionally, to improve the compression efficiency of the codec, we propose incorporating the quality map of the preceding frame into the hyperprior encoder and leveraging the temporal prior encoder. A multi-stage training strategy is employed to jointly train the codec with a multi-frame rate-distortion loss function. The experimental results demonstrate that the proposed method can achieve continuously variable bitrate adaptation while maintaining rate-distortion performance comparable to the fixed bitrate model. Furthermore, the proposed method also supports ROI-based compression.