Visual saliency on stereoscopic 3D (S3D) images has been shown to be heavily influenced by image quality. Hence, this dependency is an important factor in image quality prediction, image restoration and discomfort reduction, but it is still very difficult to predict such a nonlinear relation in images. In addition, most algorithms specialized in detecting visual saliency on pristine images may unsurprisingly fail when facing distorted images. In this paper, we investigate a deep learning scheme named Deep Visual Saliency (DeepVS) to achieve a more accurate and reliable saliency predictor even in the presence of distortions. Since visual saliency is influenced by low-level features (contrast, luminance, and depth information) from a psychophysical point of view, we propose seven low-level features derived from S3D image pairs and utilize them in the context of deep learning to detect visual attention adaptively to human perception. During analysis, it turns out that the low-level features play a role to extract distortion and saliency information. To construct saliency predictors, we weight and model the human visual saliency through two different network architectures, a regression and a fully convolutional neural networks. Our results from thorough experiments confirm that the predicted saliency maps are up to 70% correlated with human gaze patterns, which emphasize the need for the hand-crafted features as input to deep neural networks in S3D saliency detection.
Bibliographical noteFunding Information:
Manuscript received November 25, 2017; revised July 3, 2018 and September 5, 2018; accepted October 21, 2018. Date of publication November 2, 2018; date of current version December 12, 2018. This work was supported by the Samsung Research Funding Center of Samsung Electronics under Project SRFC-IT1702-08. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ce Zhu. (Corresponding author: Sanghoon Lee.) A.-D. Nguyen and S. Lee are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: firstname.lastname@example.org; email@example.com).
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All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design