  Stereo video retargeting aims to resize an image to a desired aspect ratio.
The quality of retargeted videos can be significantly impacted by the stereo
videos spatial, temporal, and disparity coherence, all of which can be impacted
by the retargeting process. Due to the lack of a publicly accessible annotated
dataset, there is little research on deep learning-based methods for stereo
video retargeting. This paper proposes an unsupervised deep learning-based
stereo video retargeting network. Our model first detects the salient objects
and shifts and warps all objects such that it minimizes the distortion of the
salient parts of the stereo frames. We use 1D convolution for shifting the
salient objects and design a stereo video Transformer to assist the retargeting
process. To train the network, we use the parallax attention mechanism to fuse
the left and right views and feed the retargeted frames to a reconstruction
module that reverses the retargeted frames to the input frames. Therefore, the
network is trained in an unsupervised manner. Extensive qualitative and
quantitative experiments and ablation studies on KITTI stereo 2012 and 2015
datasets demonstrate the efficiency of the proposed method over the existing
state-of-the-art methods. The code is available at
https://github.com/z65451/SVR/.
