  Recent studies of deep learning based stereo image super-resolution
(StereoSR) have promoted the development of StereoSR. However, existing
StereoSR models mainly concentrate on improving quantitative evaluation metrics
and neglect the visual quality of super-resolved stereo images. To improve the
perceptual performance, this paper proposes the first perception-oriented
stereo image super-resolution approach by exploiting the feedback, provided by
the evaluation on the perceptual quality of StereoSR results. To provide
accurate guidance for the StereoSR model, we develop the first special stereo
image super-resolution quality assessment (StereoSRQA) model, and further
construct a StereoSRQA database. Extensive experiments demonstrate that our
StereoSR approach significantly improves the perceptual quality and enhances
the reliability of stereo images for disparity estimation.

Image and Video Processing;Computer Vision and Pattern Recognition