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

Abstract

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. INDEX TERMS Adaptive quantization parameter, deep neural network, high efficiency video coding (HEVC), perceptual quantization parameter, VGG-16 network, video coding.

Optimal CTU-Level Rate Control Model for HEVC Based on Deep Convolutional Features

“…This paper presents the development of a new estimation model for the α and β parameters as well as estimation models for the bit allocation, parameter λ, QP decision, and boundary adjustment of both λ and QP for the CTU-level rate control in the HEVC encoder. Furthermore, an adaptive QP decision at the frame-level [23] is also used for the proposed CTU-level rate control algorithm. The proposed algorithm is designed to impove the rate control in HM-16.20 reference software as its performance is significantly degraded with rate control enabled.…”

“…Since rate control algorithms also rely on the initial QP of a frame, the proposed CTU-level rate control takes into account our previous study of perceptual adaptive QP decision to handle the frame-level initial QP decision. Please refer to the detailed algorithm, as discussed in [23]. Visual quality of BQTerrace frame to frame Figure 2 illustrates the flow of the proposed algorithm.…”

“…Visual quality of BQTerrace frame to frame Figure 2 illustrates the flow of the proposed algorithm. The proposed CTU-level rate control algorithm is applied after the frame-level QP initialization in [23] is employed. The VGG feature for the proposed algorithm is first estimated during the CTU-level rate control loop.…”

“…Note that the CTU-level rate control is essential for regulating the bitrate allocation, improving the coding performance, and comprehensively controlling the visual quality of a compressed video. In regards to the perceptual redundancy for the coding efficiency improvements, our previous study found that a perceptually adaptive QP at frame-level can significantly improve the coding efficiency of the HM-16.20 by taking advantage of the subjective characteristics of a video sequence [23].…”

“…In this study, the proposed algorithm establishes a new model for α and β estimations to improve the correlation between them and the estimated λ, based on the high-level features and QP values of previously coded CTUs. As in our previous study [23], high-level features are obtained from the original and reconstructed CTUs using a pre-trained visual geometry group (VGG-16) network model [31], [23]. Based on the proposed models for estimating α and β, the R-λ estimation for each CTU is then reformulated to minimize distortion over the entire frame at a given target bitrate.…”