CNN-based rate-distortion modeling for H.265/HEVC
Bin Xu, Xiang Pan, Yan Zhou, Yiming Li, Daiqin Yang, Zhenzhong Chen · 2017
In this paper, we propose a convolutional neural network (CNN)-based rate-distortion (R-D) modeling method for H.265/HEVC. A fully convolutional neural network (CNN) is designed to learn end-to-end, pixels-to-pixels mappings from the original images to the structural similarity (SSIM) maps indicating distortion. The rate information is predicted through a CNN with fully connected layers as well. When compared to traditional CNN methods, the proposed mappings to the distortion or rate information. The experiments demonstrate the feasibility of our CNN-based framework for rate-distortion modeling.