Abstract
Temporal HVS characteristics are not fully exploited in conventional JND models. In this paper, we improve the spatio-temporal JND model by fully leveraging the temporal HVS characteristics. From the viewpoint of visual attention, we investigate two related factors, positive stimulus saliency and negative uncertainty. This paper measures the stimulus saliency according to two stimulus-driven parameters, relative motion and duration along the motion trajectory, and measures the uncertainty according to two uncertainty-driven parameters, global motion and residue intensity fluctuation. These four different parameters are measured with self-information and information entropy, and unified for fusion with homogeneity. As a result, a novel temporal JND adjustment weight model is proposed. Finally, we fuse the spatial JND model and temporal JND weight to form the spatio-temporal JND model. The experiment results verify that the proposed JND model yields significant performance improvement with much higher capability of distortion concealment compared to state-of-the-art JND profiles.
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Acknowledgement
This work was supported by the Natural Science Foundation of China (NSFC) under Grants 61572449, 61931008, 61901150 and 61972123,Key R&D projects 2018YFC0830106, and by Natural Science Foundation of Zhejiang Province under Grants Q19F010030 and Y19F020124.
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Yin, H., Xing, Y., Xia, G., Huang, X., Yan, C. (2020). Improving Just Noticeable Difference Model by Leveraging Temporal HVS Perception Characteristics. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_8
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DOI: https://doi.org/10.1007/978-3-030-37731-1_8
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