A CNN cascade for quality enhancement of compressed depth images

Abstract

Transmitting depth images along with the corresponding textures enables a wide range of receiver-side 3D applications. Since each pixel on the depth images represents a corresponding 3D scene geometric information, when compressed during transmission the compression artifacts will lead to severe geometry distortions and visual perceptual degradation. To solve this problem, in this paper we proposed a convolutional neural network (CNN) cascade for suppressing the compression artifacts on depth images. According to the feature of depth images, we furthermore, adopt a weighted loss function for network training which can adaptively improve the learning efficiency and accuracy. Meanwhile, in order to overcome the limited training data problem, we audaciously trained our network on textures first and then finetune on the target depth images. To our best knowledge, few works have applied CNN on depth images targeting for compression artifacts reduction (CAR). Through extensive experiments, our proposed solution achieves higher quality for both reconstructed depth images and synthesized virtual views than the state-of-the-art methods.