Just Noticeable Difference for 3D Images with Depth Saliency

Zhong, Rui; Hu, Ruimin; Shi, Yi; Wang, Zhongyuan; Han, Zhen; Liu, Lu; Hu, Jinhui

doi:10.1007/978-3-642-34778-8_38

Cited by 5 publications

(1 citation statement)

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“…It has been demonstrated that depth perception is influenced not only by depth intensity (DI) but also by depth contrast (DC). In light of this, Zhong et al [80] first proposed a 3D image JND model combining 2D-JND with depth saliency taking DI and DC into account. Moreover, the serious geometric distortion (GD) in synthesized views attracts visual attention leading to smaller JND thresholds.…”

Section: F Hjnd Modelmentioning

confidence: 99%

A Survey of Stereoscopic 3D Just Noticeable Difference Models

et al. 2019

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Just noticeable difference (JND) for stereoscopic 3D content reflects the maximum tolerable distortion; it corresponds to the visibility threshold of the asymmetric distortions in the left and right contents. The 3D-JND models can be used to improve the efficiency of the 3D compression or the 3D quality assessment. Compared to 2D-JND models, the 3D-JND models appeared recently and the related literature is rather limited. In this paper, we give a deep and comprehensive study of the pixel-based 3D-JND models. To our best knowledge, this is the first review on 3D-JND models. Each model is briefly described by giving its rationale and main components in addition to providing exhaustive information about the targeted application, the pros, and cons. Moreover, we present the characteristics of the human visual system presented in these models. In addition, we analyze and compare the 3D-JND models thoroughly using qualitative and quantitative performance evaluation based on Middlebury stereo datasets. Besides, we measure the JND thresholds of the asymmetric distortion based on psychophysical experiments and compare these experimental results to the estimates from the 3D-JND models in order to evaluate the accuracy of each model. INDEX TERMS Human visual system, just noticeable difference (JND), 3D compression, 3D-JND models, 3D quality assessment.

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Section: F Hjnd Modelmentioning

confidence: 99%