In this paper, an error concealment scheme for neural-network based compression of depth image in 3D videos is proposed. In the neural-network based compression, each depth image is represented by one or more neural networks. The advantage of neural-network based compression lies in the parallel processing ability of multiple neurons, which can handle the massive data volume of 3D videos. The similarity of neuron weights of neighboring nodes is exploited to recover the loss neuron weights when transmitting with an error-prone communication channel. With a simulated noisy channel, the quality of compressed 3D video, which is reconstructed undergoing the noisy channel, can be recovered well by the proposed error concealment scheme.
Object-Based 2D-to-3D Video Conversion for Effective Stereoscopic Content Generation in 3D-TV Applications