LSVC: A Learning-based Stereo Video Compression Framework
Zhenghao Chen, Guo Lu, Zhihao Hu, Shan Liu, Wei Jiang, Dong Xu
Abstract
In this work, we propose the first end-to-end optimized framework for compressing automotive stereo videos (i.e., stereo videos from autonomous driving applications) from both left and right views. Specifically, when compressing the current frame from each view, our framework reduces temporal redundancy by performing motion compensation using the reconstructed intra-view adjacent frame and at the same time exploits binocular redundancy by conducting disparity compensation using the latest reconstructed cross-view frame. Moreover, to effectively compress the introduced motion and disparity offsets for better compensation, we further propose two novel schemes called motion residual compression and disparity residual compression to respectively generate the predicted motion offset and disparity offset from the previously compressed motion offset and disparity offset, such that we can more effectively compress residual offset information for better bit-rate saving. Overall, the entire framework is implemented by the fully-differentiable modules and can be optimized in an end-to-end manner. Our comprehensive experiments on three automotive stereo video benchmarks Cityscapes, KITTI 2012 and KITTI 2015 demonstrate that our proposed framework outperforms the learning-based single-view video codec and the traditional hand-crafted multi-view video codec.