Anh-Duc Nguyen Kim, Jongyoo Oh, Heeseok Kim, Haksub Lin, Weisi Lee, Sanghoon 2024-01-19T20:31:23Z 2024-01-19T20:31:23Z 2021-09-02 2019-04 1057-7149 https://pubs.kist.re.kr/handle/201004/120177 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. English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PREDICTION ATTENTION QUALITY MODEL Deep Visual Saliency on Stereoscopic Images Article 10.1109/TIP.2018.2879408 1 IEEE TRANSACTIONS ON IMAGE PROCESSING, v.28, no.4, pp.1939 - 1953 IEEE TRANSACTIONS ON IMAGE PROCESSING 28 4 1939 1953 scie scopus 000453552100004 2-s2.0-85056147202 Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Computer Science Engineering Article PREDICTION ATTENTION QUALITY MODEL Saliency prediction stereoscopic image distorted image convolutional neural network deep learning