Deep Network-Based Adaptive Quantization for Practical Video Coding

The optimization of block-level quantization parameters (QP) is critical to improving the performance of practical block-based video compression encoders, but the extremely large optimization space makes it challenging to solve. Existing solutions, e.g. HEVC encoder x265, usually add some optimization constraints of the block-independent assumption and linear distortion propagation model, which limits compression efficiency improvement to a certain extent. To address this problem, a deep learning-based encoder-only adaptive quantization method (DAQ) is proposed in this paper, where a deep network is designed to adaptively model the joint temporal propagation relationship of quantization among blocks. Specifically, DAQ consists of two phases: in the training phase, considering the heavy searching cost of the traditional codec, we introduce a well-designed end-to-end learned block-based video compression network as an effective training proxy tool for the deep encoder-side network. While in the deployment phase, the trained deep network is applied to jointly predict all block QPs in a frame for the traditional encoder. Besides, our network deploys only on the encoder side without changing the standard decoder and has very low inference complexity, making it able to apply in practice. At last, we deploy DAQ in HEVC and VVC encoder for performance comparison, and the experimental results demonstrate that DAQ significantly outperforms practically used x265 with on average 15.0%, 10.9% BD-rate reduction under the SSIM and PSNR, and also achieves 12.5%, 5.0% coding gain than VTM. Moreover, for deploying deep video codec in practice, this work provides a new insight for optimizing the encoder parameters with a large space.