Data-Driven Rate Control for Rate-Distortion Optimization in HEVC Based on Simplified Effective Initial QP Learning

Different from the conventional calculative methods, a learning-based initial quantization parameter (LIQP) method is proposed in this paper to improve rate control of high efficiency video coding (H.265). First, the framework for initial quantization parameter (QP) learning is proposed, where a novel equivalent approach to build the benchmark labels is proposed using the single rate-distortion (R-D) pair in each initial QP testing. With the criterion of maximizing the prediction accuracy of initial QPs, features and parameters of the learning model are refined. Instead of the traditionally used target bits per pixel (bpp) for intraframe, the target bpp for all remaining frames is proposed to avoid the empirical setting on intracoding bits, and thus the related inaccuracy can be prevented. We clearly present the motivations of the proposed LIQP method, as well as the reasons for the extracted features and model parameters. The proposed LIQP method outperforms the latest HM-16.14 by achieving significant gains on R-D performance (−15.48% BD-BR and 0.782 dB BD-PSNR gains), quality smoothness (1.581 dB versus 2.598 dB), and more stable buffer occupancy control, with similar high bit rate accuracy (99.84% versus 99.87%), and can also work well for scene change cases.