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Proceedings Paper

A perceptual preprocess method for 3D-HEVC
Author(s): Yawen Shi; Yongfang Wang; Yubing Wang
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Paper Abstract

A perceptual preprocessing method for 3D-HEVC coding is proposed in the paper. Firstly we proposed a new JND model, which accounts for luminance contrast masking effect, spatial masking effect, and temporal masking effect, saliency characteristic as well as depth information. We utilize spectral residual approach to obtain the saliency map and built a visual saliency factor based on saliency map. In order to distinguish the sensitivity of objects in different depth. We segment each texture frame into foreground and background by a automatic threshold selection algorithm using corresponding depth information, and then built a depth weighting factor. A JND modulation factor is built with a linear combined with visual saliency factor and depth weighting factor to adjust the JND threshold. Then, we applied the proposed JND model to 3D-HEVC for residual filtering and distortion coefficient processing. The filtering process is that the residual value will be set to zero if the JND threshold is greater than residual value, or directly subtract the JND threshold from residual value if JND threshold is less than residual value. Experiment results demonstrate that the proposed method can achieve average bit rate reduction of 15.11%, compared to the original coding scheme with HTM12.1, while maintains the same subjective quality.

Paper Details

Date Published: 5 August 2015
PDF: 8 pages
Proc. SPIE 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 962219 (5 August 2015); doi: 10.1117/12.2193292
Show Author Affiliations
Yawen Shi, Shanghai Univ. (China)
Yongfang Wang, Shanghai Univ. (China)
Yubing Wang, Shanghai Univ. (China)

Published in SPIE Proceedings Vol. 9622:
2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
Guangming Shi; Xuelong Li; Bormin Huang, Editor(s)

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