3D hybrid just noticeable distortion modeling for depth image-based rendering

The 3D Just Noticeable Distortion (JND) threshold in essence depends on Human Visual Sensitivity (HVS). This paper carves out a Hybrid Just Noticeable Distortion (HJND) model to measure JND threshold in the framework of Depth Image-Based Rendering (DIBR) for 3D video. The critical differences betwee...

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Vydané v:	Multimedia tools and applications Ročník 74; číslo 23; s. 10457 - 10478
Hlavní autori:	Zhong, Rui, Hu, Ruimin, Wang, Zhongyuan, Wang, Shizheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.12.2015 Springer Nature B.V
Predmet:	3-D graphics Analysis Computer Communication Networks Computer Science Control algorithms Data Structures and Information Theory Distortion Efficiency Eyes & eyesight Influence Mathematical models Multimedia Multimedia computer applications Multimedia Information Systems Rendering Special Purpose and Application-Based Systems Studies Three dimensional Three dimensional models Thresholds Visual Visual perception 3D Just Noticeable Distortion (3DJND) Depth saliency Geometric distortion Human Visual Sensitivity (HVS) Depth Image-Based Rendering (DIBR)
ISSN:	1380-7501, 1573-7721
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Abstract	The 3D Just Noticeable Distortion (JND) threshold in essence depends on Human Visual Sensitivity (HVS). This paper carves out a Hybrid Just Noticeable Distortion (HJND) model to measure JND threshold in the framework of Depth Image-Based Rendering (DIBR) for 3D video. The critical differences between 2D and 3D visual perception, depth saliency and geometric distortion, are combined into the HJND model because their significant influence on HVS. To save bit, the HJND model is introduced into the Multi-view Video plus Depth (MVD) encoding framework as a residual filter. After the residue is filtered by HJND and the reference model named Joint Just Noticeable Distortion (JJND), bit saving is achieved up to 28.79% and 23.53%, respectively, and the 3D impaired videos filtered by HJND and JJND have the similar subjective quality. The experiments demonstrate that HJND describes HVS for 3D video more accurately than the state-of-the-art methods.
AbstractList	The 3D Just Noticeable Distortion (JND) threshold in essence depends on Human Visual Sensitivity (HVS). This paper carves out a Hybrid Just Noticeable Distortion (HJND) model to measure JND threshold in the framework of Depth Image-Based Rendering (DIBR) for 3D video. The critical differences between 2D and 3D visual perception, depth saliency and geometric distortion, are combined into the HJND model because their significant influence on HVS. To save bit, the HJND model is introduced into the Multi-view Video plus Depth (MVD) encoding framework as a residual filter. After the residue is filtered by HJND and the reference model named Joint Just Noticeable Distortion (JJND), bit saving is achieved up to 28.79% and 23.53%, respectively, and the 3D impaired videos filtered by HJND and JJND have the similar subjective quality. The experiments demonstrate that HJND describes HVS for 3D video more accurately than the state-of-the-art methods.
Author	Wang, Shizheng Hu, Ruimin Zhong, Rui Wang, Zhongyuan
Author_xml	– sequence: 1 givenname: Rui surname: Zhong fullname: Zhong, Rui organization: National Engineering Research Center for Multimedia Software and the School of Computer, Wuhan University – sequence: 2 givenname: Ruimin surname: Hu fullname: Hu, Ruimin email: hurm1964@gmail.com organization: National Engineering Research Center for Multimedia Software and the School of Computer, Wuhan University – sequence: 3 givenname: Zhongyuan surname: Wang fullname: Wang, Zhongyuan organization: National Engineering Research Center for Multimedia Software and the School of Computer, Wuhan University – sequence: 4 givenname: Shizheng surname: Wang fullname: Wang, Shizheng organization: School of Electrical and Electronic Engineering, Nanyang Technological University
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Cites_doi	10.1109/TCSVT.2013.2240919 10.1109/34.730558 10.1016/j.image.2005.04.001 10.1016/j.image.2008.10.010 10.1002/mrm.1910340111 10.1109/TCSVT.2007.903665 10.1109/LSP.2010.2090041 10.1109/TCSVT.2010.2087432 10.1109/ICME.2010.5583897 10.1109/VCIP.2011.6116016 10.1109/ICME.2010.5582582 10.1109/TBC.2011.2128250 10.1109/76.488822 10.1109/ISCAS.2011.5938025 10.1109/76.475889 10.2200/S00010ED1V01Y200508IVM003 10.1109/ICIP.2008.4712288
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Keywords	3D Just Noticeable Distortion (3DJND) Depth saliency Geometric distortion Human Visual Sensitivity (HVS) Depth Image-Based Rendering (DIBR)
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Snippet	The 3D Just Noticeable Distortion (JND) threshold in essence depends on Human Visual Sensitivity (HVS). This paper carves out a Hybrid Just Noticeable...
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SubjectTerms	3-D graphics Analysis Computer Communication Networks Computer Science Control algorithms Data Structures and Information Theory Distortion Efficiency Eyes & eyesight Influence Mathematical models Multimedia Multimedia computer applications Multimedia Information Systems Rendering Special Purpose and Application-Based Systems Studies Three dimensional Three dimensional models Thresholds Visual Visual perception
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Title	3D hybrid just noticeable distortion modeling for depth image-based rendering
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