Novel 3D video conversion from down-sampled stereo video

Stereo video has become the main-stream 3D video format in recent years due to its simplicity in data representation and acquisition. Under stereo settings, the twin problems of video super-resolution and high-resolution disparity estimation are intertwined. In this paper, we present a novel 3D video conversion system that converts down-sampled stereo video to high-resolution stereo sequences with a Bayesian framework. In addition, we estimate the finer-resolution disparity maps with a two-step CRF model. Our super-resolution system can also be incorporated into the video coding process, which can significantly lower the data amount as well as preserving high-quality details. Experimental results demonstrate that our system can enhance image resolution in both stereo video and disparity map. Objective evaluation of the proposed video coding scheme combined with super-resolution at different compression ratios also shows competitive performance of proposed system for video compression.