结合人眼对比度敏感视觉特性的视频质量客观评价

姚军财; 刘贵忠

doi:10.3788/OPE.20162403.0659

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结合人眼对比度敏感视觉特性的视频质量客观评价

信息科学 | 更新时间：2020-08-13

- 结合人眼对比度敏感视觉特性的视频质量客观评价
- Video quality objective assessment combined contrast sensitivity characteristics of human visual system
- 光学精密工程 2016年24卷第3期页码：659-667
- 作者机构：
  
  1. 陕西理工学院物理与电信工程学院, 陕西汉中 723000
  2. 西安交通大学电子与信息工程学院,陕西西安,710049
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.61301237)();陕西省科技新星计划资助项目(No.2015KJXX-42)()
- DOI：10.3788/OPE.20162403.0659
  中图分类号： R339.14;TN919.81
- 收稿日期：2015-12-08，
  
  修回日期：2016-01-14，
  
  纸质出版日期：2016-03-25
- 稿件说明：
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姚军财, 刘贵忠,. 结合人眼对比度敏感视觉特性的视频质量客观评价[J]. 光学精密工程, 2016,24(3): 659-667

YAO Jun-cai, LIU Gui-zhong,. Video quality objective assessment combined contrast sensitivity characteristics of human visual system[J]. Editorial Office of Optics and Precision Engineering, 2016,24(3): 659-667
姚军财, 刘贵忠,. 结合人眼对比度敏感视觉特性的视频质量客观评价[J]. 光学精密工程, 2016,24(3): 659-667 DOI： 10.3788/OPE.20162403.0659.

YAO Jun-cai, LIU Gui-zhong,. Video quality objective assessment combined contrast sensitivity characteristics of human visual system[J]. Editorial Office of Optics and Precision Engineering, 2016,24(3): 659-667 DOI： 10.3788/OPE.20162403.0659.

摘要

结合人眼对亮度、色度、对比度以及运动目标的感知特性

提出了一种基于人眼对视频内容感知的视频质量客观评价方法。该方法将视频分为空域和时域信息分别描述

并利用人眼感知特性

从视频的亮度、色度、对比度以及目标运动4个方面提取特征

计算其强度。然后以人眼对比度敏感值作为强度的权重因子求和

构建人眼感知视频内容模型。最后

分别以此模型模拟人眼感知源视频和失真后的视频

计算每对应单元的所有像素之间和运动矢量之间的强度差;以强度差作为视频质量评价的分数

构建视频质量客观评价模型。采用LIVE数据库中的6个源视频和48个测试视频进行了质量评价实验

并与视频质量专家组(VQEG)推荐的5个较好的视频质量客观评价模型进行了对比分析。结果表明:提出模型的视频质量评价结果与主观评价结果之间的线性相关性系数达到0.8705

显示了较好的一致性

评价效果优于5个典型的模型。

Abstract

In combination of the perceiving characteristics of human eyes for brightness

chroma

contrast and moving targets

an objective assessment method of video quality based on contrast sensitivity characteristics of a human visual system was proposed. In the method

the video was divided into spatial and time domains to be described. The features of image were extracted from four aspects

brightness

chroma

contrast

and target motion based on the perceiving characteristics of human eyes and their intensities were computed. Then

the contrast sensitivity values of human eyes were used as the weight factors of the intensity to sum and to construct the model of human eye perception content of the video. Finally

original and distorted videos respectively perceived by imitating eyes with this model

and the intensity differences of the pixels and the motion vectors between arbitrary corresponding units of two videos were computed. By taking the intensity differences as the scores of video quality objective evaluation

the objective evaluation model for video quality was constructed by them. The experiments were carried out with 6 source videos and 48 test videos proposed by LIVE database

and the 5 classic video quality evaluation models recommended by the Video Quality Expert Group(VQEG) were compared with the proposed model. The results show that the linear correlation coefficient between video quality evaluated by the proposed model and the subjective evaluation results reaches 0.8705. They have good consistency

and evaluation effects are better than those of other 5 classical models.

关键词

Keywords

references

范媛媛, 沈湘衡, 桑英军. 基于对比度敏感度的无参考图像清晰度评价[J]. 光学精密工程, 2011, 19(10):2485-2493. FAN Y Y, SHEN X H, SAN Y J. No reference image sharpness assessment based on contrast sensitivity[J]. Opt. Precision Eng., 2011, 19(10):2485-2493.(in Chinese)

宁方立, 何碧静, 韦娟. 基于l_p范数的压缩感知图像重建算法研究[J]. 物理学报, 2013, 62(17):174212. NING F L, HE B J, WEI J. An algorithm for image reconstruction based on l_p norm[J]. Acta Phys. Sin. , 2013, 62(17):174212.(in Chinese)

CHEN Y J, WU K S, ZHANG Q. From QoS to QoE:A tutorial on video quality assessment[J]. IEEE Communications Surveys & Tutorials , 2015, 17(2):1126-1165.

KARTHIKEYAN R, SAINARAYANAN G, DEEPA S N. Perceptual video quality assessment in H. 264 video coding standard using objective modeling[J]. Springerplus, 2014, 3(1):1-6.

杨亚威, 李俊山, 张士杰, 等. 基于生物视觉标准模型特征的无参考型图像质量评价方法[J]. 液晶与显示, 2014, 29(6):1016-1023 YANG Y W, LI J S, ZHANG S J, et al. Non reference image quality assessment approach based on standard model features of biological vision[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(6):1016-1023.(in Chinese)

米曾真. 小波域中CSF频率与方向加权的图像质量评价方法[J]. 电子学报, 2014, 42(7):1273-1276. MI Z ZH. Image quality evaluation method based on frequency and direction weighted to CSF in wavelet domain[J]. Acta Electronic Sinica, 2014, 42(7):1273-1276.(in Chinese)

LI C F, BOVIK A C. Content-weighted video quality assessment using a three-component image model[J]. Journal of Electronic Imaging, 2010, 19(1):143-153.

邱聚能, 李辉, 闫乐乐, 等. 基于图像质量评价的LCD运动模糊检测方法[J]. 液晶与显示, 2015, 30(3):531-537 QIU J N, LI H, YAN L L, et al.. LCD motion blur detecting method based on image quality assessment[J]. Chinese Journal of Liquid Crystals and Displays, 2015, 30(3):531-537.(in Chinese)

OU Y F, XUE Y Y, WANG Y. Q-STAR_A perceptual video quality model considering impact of spatial, temporal, and amplitude resolution[J]. IEEE Transactions on Image Processing, 2014, 23(6):2473-2486.

WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment:From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4):600-612.

AKAMINE A W Y, FARIAS M C. Video quality assessment using visual attention computational models[J]. Journal of Electronic Imaging, 2014, 23(6):061107.

SOUNDARARAJAN R, BOVIK A C. Video quality assessment by reduced reference spatio-temporal entropic differencing[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2013, 23(4):684-694.

MOORTHY A K, CHOI L K, BOVIK A C, et al.. Video quality assessment on mobile devices:subjective, behavioral and objective studies[J]. IEEE Journal of Selected Topics in Signal Processing, 2012, 6(6):652-671.

ANEGEKUH L, SUN L, JAMMEH E, et al.. Content-based video quality prediction for HEVC encoded videos streamed over packet networks[J]. IEEE Transactions on Multimedia, 2015, 17(8):1323-1334.

GINESU G, MASSIDDA F, GIUSTO D D. A multi-factors approach for image quality assessment based on a human visual system model[J]. Signal Processing:Image Communication, 2006, 21(4):316-333.

SESHADRINATHAN K, SOUNDARARAJAN R, BOVIK A C, et al.. LIVE Video Quality Database[DB] http://live.ece.utexas.edu/research/quality/live_mobile_video.html,[2016-04-20].

BARTEN P G J. Evaluation of subjective image quality with the square-root integral method[J]. Journal of the Optical Society of America A-Optics Image Science and Vision, 1990, 7(10):2024-2031

NADENAU M. Integration of human color vision models into high quality image compression[D]. Switzerland:cole Polytechnique Fédérale de Lausanne. 2000.

KELLY D H. Motion and vision. Ⅱ. Stabilized spatio-temporal threshold surface[J]. Journal of the Optical Society of America, 1979, 69(10):1340-1349.

ZHANG F, BULL D R. Quality assessment methods for perceptual video compression[C]. 20th IEEE International Conference on Image Processing(ICIP 2013), 2013:39-43.

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