Authors:
Chenxi Ma; Bo Yan; Weimin Tan; Xuhao Jiang
Publication:
This paper is included in the Proceedings of the 29th ACM International Conference on Multimedia (ACM MM), October 20–24, 2021
Abstract:
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 the quantitative evaluation metrics and neglect the visual quality of the 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.