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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Bibliographic Details
Published in:Multimedia tools and applications Vol. 74; no. 23; pp. 10457 - 10478
Main Authors: Zhong, Rui, Hu, Ruimin, Wang, Zhongyuan, Wang, Shizheng
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2015
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-014-2176-y