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[1]李素梅,王明毅,赵 平,等.基于投影权值归一化的立体图像质量评价方法[J].天津大学学报(自然科学与工程技术版),2020,53(03):252-258.[doi:10.11784/tdxbz201901029]
 Li Sumei,Wang Mingyi,Zhao Ping,et al.Stereoscopic Image Quality Assessment Method Using Projection-Based Weight Normalization[J].Journal of Tianjin University(Science and Technology),2020,53(03):252-258.[doi:10.11784/tdxbz201901029]
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基于投影权值归一化的立体图像质量评价方法

参考文献/References:

参考文献:

[1] Zilly F,Kluger J,Kauff P. Production rules for stereo acquisition[J]. Proceedings of the IEEE,2011,99(4):590-606.

[2] Urey H,Chellappan K V,Erden E,et al. State of the art in stereoscopic and autostereoscoic displays[J]. Proceedings of the IEEE,2011,99(4):540-555.

[3] 毛香英,郁 梅,蒋刚毅,等. 基于结构失真分析的立体图像质量客观评价模型[J]. 计算机辅助设计与图形学学报,2012,24(8):1047-1056.

Mao Xiangying,Yu Mei,Jiang Gangyi,et al. Objec-tive evaluation model of stereo image quality based on structural distortion analysis[J]. Journal of Computer-Aided Design & Computer Graphics,2012,24(8):1047-1056(in Chinese).

[4] 徐姝宁,李素梅. 基于视觉显著性的立体图像质量评价方法[J]. 信息技术,2016(10):91-93.

Xu Shuning,Li Sumei. Stereo image quality evalua-tion method based on visual saliency[J]. Information Technology,2016(10):91-93(in Chinese).

[5] Bensalma R,Larabi M C. A perceptual metric for ste-reoscopic image quality assessment based on the bin-ocular energy[J]. Multidimensional Systems and Signal Processing,2013,24(2):281-316.

[6] Shao Feng,Jiang Gangyi,Yu Mei,et al. Binocular energy response based quality assessment of stereo-scopic images[J]. Digital Signal Processing,2014,29:45-53.

[7] Shao Feng,Lin Weisi,Wang Shanshan,et al. Learn-ing receptive fields and quality lookups for blind quali-ty assessment of stereoscopic images[J]. IEEE Trans-actions on Cybernetics,2016,46(3):730-743.

[8] 王光华,李素梅,朱 丹,等. 极端学习机在立体图像质量客观评价中的应用[J]. 光电子·激光,2014(9):1837-1842.

Wang Guanghua,Li Sumei,Zhu Dan,et al. Applica-tion of extreme learning machine in objective evalua-tion of stereo image quality[J]. Optoelectronics·Laser,2014(9):1837-1842(in Chi-nese).

[9] 顾珊波,邵 枫,蒋刚毅,等. 基于支持向量回归的立体图像客观质量评价模型[J]. 电子与信息学报,2012,34(2):368-374.

Gu Shanbo,Shao Feng,Jiang Gangyi,et al. Objec-tive quality evaluation model of stereo image based on support vector regression[J]. Journal of Electronics & Information Technology,2012,34(2):368-374(in Chinese).

[10] 吴限光,李素梅,程金翠. 基于遗传神经网络的立体图像的客观评价[J]. 信息技术,2013(5):148-153.

Wu Xianguang,Li Sumei,Cheng Jincui,et al. Ob-jective quality evaluation method of stereo image based on genetic algorithm and neural network[J]. Infor-mation Technology,2013(5):148-153(in Chinese).

[11] Zhang Wei,Qu Chenfei,Lin Ma,et al. Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network[J]. Pat-tern Recognition,2016,59(SI):176-187.

[12] 陈 慧,李朝锋. 深度卷积神经网络的立体彩色图像质量评价[J]. 计算机科学与探索,2018,12(8):1315-1322.

Chen Hui,Li Chaofeng. Stereoscopic color image quality evaluation of deep convolutional neural net-works[J]. Computer Science and Exploration,2018,12(8):1315-1322(in Chinese).

[13] Ding Yong,Deng Ruizhe,Xie Xin,et al. Reference stereoscopic image quality assessment using convolu-tional neural network for adaptive feature extrac-tion[J]. IEEE Access,2018(6):37595-37603.

[14] Hubel D H,Wiesel T N. Receptive fields of singleneu-rones in the cat’s striate cortex[J]. The Journal of Physiology,1959,148(3):574-591.

[15] Lin Yancong,Yang Jiachen,Lu Wen,et al. Quality index for stereoscopic images by jointly evaluating cy-clopean amplitude and cyclopean phase[J]. IEEE Journal of Selected Topics in Signal Processing,2017,11(11):89-101.

[16] Oh H,Ahn S,Kim J,et al. Blind deep S3D image quality evaluation via local to global feature aggrega-tion[J]. IEEE Transactions on Image Processing,2017,26(10):4923-4936.

[17] Ding Jian,Klein S A,Levi D M. Binocular combina-tion of phaseand contrast explained by a gain-control and gain-enhancement model[J]. Journal of Vision,2013,13(2):1-37.

[18] Hinton G E,Simon O,Teh Y W. A fast learning algo-rithm for deep belief nets[J]. Neural Computation,2006,18(7):1527-1554.

[19] Szegedy C,Liu Wei,Jia Yangqing,et al. Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Boston,USA,2015:1-9.

[20] He Kaiming,Zhang Xiangyu,Ren Shaoqing,et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recogni-tion(CVPR). Las Vegas,USA,2016:770-778.

[21] Huang Lei,Liu Xianglong,Lang Bo,et al. Projection Based Weight Normalization for Deep Neural Net-works[EB/OL]. http://arxiv.org/pdf/1710.02338.PDF,2017-10-06.

[22] Salimans T,Kingma D P. Weight normalization:A simple reparameterization to acceleratetraining of deep neural networks[C] //Conference and Workshop on Neural Information Processing Systems(NIPS). Barce-lona,Spain,2016:901-909.

[23] Ioffe S,Szegedy C. Batch normalization:Accelerating deep network training by reducing internal covariate shift[C] //International Conference on Ma-chine Learning(ICML). Lille,France,2015:448-456.

[24] Moorthy A K,Su Chechun,Mittal A,et al. Subjec-tive evaluation of stereoscopic image quality[J]. Signal Processing Image Communication,2013,28(8):870-883.


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备注/Memo

通信作者:李素梅,lisumei@tju.edu.cn.

更新日期/Last Update: 2020-03-04