Perceptual Adaptive Quantization Parameter Selection Using Deep Convolutional Features for HEVC Encoder

In this paper, we propose a perceptual adaptive quantization based on a deep neural network on high efficiency video coding (HEVC) for bitrate reduction while maintaining subjective visual quality. The proposed algorithm adaptively determines frame-level QP values for different picture types of the hierarchical coding structure in HEVC by taking into account the high-level features extracted from the original and previously reconstructed pictures. A predefined model based on the visual geometry group (VGG-16) network is exploited to extract the high-level features for subjective visual characteristics. Furthermore, the Lagrange multiplier for each frame is also adaptively determined by involving the proposed features for deciding the appropriate parameter of the Lagrange multiplier that can be used for rate-distortion optimization during the encoding process. Experimental results reveal that the proposed perceptual adaptive QP selection can facilitate bitrate savings up to 65.73% and 47.68% and improve the BD-rate based on SSIM by approximately 20.68% and 14.27% under low-delay-P and random-access coding structures, respectively, with very minimal visual quality degradation when compared to HM-16.20 without adaptive QP selection.