CN106803952B - 结合jnd模型的交叉验证深度图质量评价方法 - Google Patents
结合jnd模型的交叉验证深度图质量评价方法 Download PDFInfo
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- CN106803952B CN106803952B CN201710041375.6A CN201710041375A CN106803952B CN 106803952 B CN106803952 B CN 106803952B CN 201710041375 A CN201710041375 A CN 201710041375A CN 106803952 B CN106803952 B CN 106803952B
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- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000002790 cross-validation Methods 0.000 title claims abstract description 18
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
- H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
- H04N13/106—Processing image signals
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
- H04N2013/0074—Stereoscopic image analysis
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- Image Processing (AREA)
Abstract
Description
方法 | Tsukuba | Venus | Teddy | Cones |
AdaptBP | 1.37 | 0.21 | 7.06 | 7.92 |
WarpMat | 1.35 | 0.24 | 9.30 | 8.47 |
P-LinearS | 1.67 | 0.89 | 12.00 | 8.44 |
VSW | 1.88 | 0.81 | 13.3 | 8.85 |
BPcompressed | 3.63 | 1.89 | 13.9 | 9.85 |
Layered | 1.87 | 1.85 | 14.3 | 14.70 |
SNCC | 6.08 | 1.73 | 11.10 | 9.02 |
ReliabilityDP | 3.39 | 3.48 | 16.90 | 19.90 |
Infection | 9.54 | 5.53 | 25.10 | 21.30 |
Tsukuba | Venus | Teddy | Cones | |
皮尔逊系数 | 0.94 | 0.90 | 0.84 | 0.97 |
线性回归系数 | 0.89 | 0.80 | 0.71 | 0.93 |
Tsukuba | Venus | Teddy | Cones | |
线性回归系数 | 0.93 | 0.76 | 0.84 | 0.91 |
Claims (3)
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CN106803952B true CN106803952B (zh) | 2018-09-14 |
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Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
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CN110544233B (zh) * | 2019-07-30 | 2022-03-08 | 北京的卢深视科技有限公司 | 基于人脸识别应用的深度图像质量评价方法 |
CN110691228A (zh) * | 2019-10-17 | 2020-01-14 | 北京迈格威科技有限公司 | 基于三维变换的深度图像噪声标记方法、装置和存储介质 |
CN111402152B (zh) * | 2020-03-10 | 2023-10-24 | 北京迈格威科技有限公司 | 视差图的处理方法、装置、计算机设备和存储介质 |
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Publication number | Priority date | Publication date | Assignee | Title |
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US7006568B1 (en) * | 1999-05-27 | 2006-02-28 | University Of Maryland, College Park | 3D wavelet based video codec with human perceptual model |
EP2229786B1 (en) * | 2008-01-18 | 2012-07-25 | Thomson Licensing | Method for assessing perceptual quality |
CN103002306B (zh) * | 2012-11-27 | 2015-03-18 | 宁波大学 | 一种深度图像编码方法 |
CN103426173B (zh) * | 2013-08-12 | 2017-05-10 | 浪潮电子信息产业股份有限公司 | 一种立体图像质量的客观评价方法 |
CN103957401A (zh) * | 2014-05-12 | 2014-07-30 | 武汉大学 | 一种基于深度图像渲染的立体混合最小可感知失真模型 |
TW201601522A (zh) * | 2014-06-23 | 2016-01-01 | 國立臺灣大學 | 基於最小可覺差之感知性視訊編碼方法 |
CN104754320B (zh) * | 2015-03-27 | 2017-05-31 | 同济大学 | 一种3d‑jnd阈值计算方法 |
CN104954778B (zh) * | 2015-06-04 | 2017-05-24 | 宁波大学 | 一种基于感知特征集的立体图像质量客观评价方法 |
CN105828061B (zh) * | 2016-05-11 | 2017-09-29 | 宁波大学 | 一种基于视觉掩蔽效应的虚拟视点质量评价方法 |
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