计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 150-156.doi: 10.11896/jsjkx.190700213
朱玲莹, 桑庆兵, 顾婷婷
ZHU Ling-ying, SANG Qing-bing, GU Ting-ting
摘要: 近年来,随着深度学习在图像质量评价领域的快速发展,平面图像质量评价得到了有效的改善,但是立体图像质量评价还有待提高。为此,文中结合三分支卷积神经网络,提出了基于视差信息的无参考立体图像质量评价方法,并分析了不同视差图对模型性能的影响。该方法将左右视图以及视差图小块作为输入,自动提取特征,通过训练得到回归模型,从而实现对立体图像的预测。文中使用了5种不同立体匹配算法来生成视差图,实验结果表明使用SAD算法得到的效果最好。在立体图像库LIVE3D和MCL3D上的实验结果表明,该方法不仅适用于评估对称失真图像,还适用于非对称失真的立体图像评价。该方法在总体失真上的结果优于其他对比算法,尤其是在MCL3D图像库上,所提方法的PLCC和SROCC比其他方法高出1%和4%。实验数据表明,所提模型提高了立体图像质量评价的性能,与人类主观感知高度一致。
中图分类号:
[1] CHEN M J,LAWRENCE K,CORMACK,et al.No-Refe-rence Quality Assessment of Natural Stereopairs [J].IEEE Trans on Image Processing,2013,22(9):3379-3391. [2] BENOIT A,CALLET P L,CAMPISI P,et al.Quality Assessment of Stereoscopic Images [J].Eurasip Journal on Image & Video Processing,2008,2008(1):1-13. [3] ZHANG Y,CHANDLER D M.Learning Natural Statistics of Binocular Contrast for No Reference Quality Assessment of Stereoscopic Images [C]//IEEE International Conference on Image Processing.Washington,USA:IEEE,2018:186-190. [4] ZHOU W,YU L,QIU W,et al.Utilizing Binocular Vision to Facilitate Completely Blind 3D Image Quality Measurement [J].Signal Processing,2016,129(C):130-136. [5] APPINA B,KHAN S,CHANNAPPAYYA S.No-referenceStereoscopic Image Quality Assessment Using Natural Scene Statistics [J].Signal Processing Image Communication,2016,43:1-14. [6] ZHANG W,QU C,MA L,et al.Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network[J].Pattern Recognition,2016,59(C):176-187. [7] SHEN L,LEI J,HOU C.No-reference stereoscopic 3D imagequality assessment via combined model[J].Multimedia Tools and Applications,2017,77(7):8195-8212. [8] LI Y,HU X.No-Reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics [C]//InternationalConfe-rence on Multimedia and Image Processing.Washington,USA:IEEE Computer Society,2017:123-127. [9] LIN C T,LIU T J,LIU K H.Visual Quality Prediction on Distorted Stereoscopic Images [C]//IEEE International Conference on Image Processing.Washington,USA:IEEE,2018:3480-3484. [10] DING Y,DENG R,XIE X,et al.No-Reference Stereoscopic Image Quality Assessment Using Convolutional Neural Network for Adaptive Feature Extraction[J].IEEE Access,2018,6:37595-37603. [11] BOYKOV Y,KOLMOGOROV V.An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(9):1124-1137. [12] HIRSCHMULLER H.Stereo Processing by Semiglobal Matching and Mutual Information [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(2):328-341. [13] WEIS D,LAI S H.Fast Template Matching Based on Norma-lized Cross Correlation with Adaptive Multilevel Winner Update [J].IEEE Transactions on Image Processing,2008,17(11):2227-2235. [14] LI JIAN J Q,LI Y C.Research Progress on Calculation Method of Disparity Map in Stereo Matching [J].Remote Sensing Information,2017,32(2):7-14. [15] SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition [J].Computer Science,2014:1409-1556. [16] HARA K,SAITO D,SHOUNO H.Analysis of Function of Rectified Linear Unit Used in Deep Learning [C]//International Joint Conference on Neural Networks.Washington,USA:IEEE,2015:1-8. [17] ALAIN H,ZIOU D.Image Quality Metrics:PSNR vs. SSIM[C]//2010 International Conference on Pattern Recognition.Washington,USA:IEEE Computer Society,2010:2366-2369. [18] WANG Z,BOVIK A C,SHEIKH H R,et al.Image Quality Assessment:From Error Visibility to Structural Similarity [J].IEEE Trans on Image Processing,2004,13(4):600-612. [19] WANG X,MA L,KWONG S,et al.Quaternion representation based visual saliency for stereoscopic image quality assessment[J].Signal Processing,2018,145:202-213. [20] BENOITA.LE CALLET P,CAMPISI P,et al.Quality Assessment of StereoscopicImages [J].EURASIP Journal on Image and Video Processing,2007,2008(1):1-13. [21] RYU S,KIM D H,SOHN K.Stereoscopic Image Quality Metric Based on Binocular Perception Model [C]//IEEE International Conference on Image Processing.Washington,USA:IEEE,2013:609-612. [22] TIAN S,ZHANG L,MORIN L,et al.NIQSV:ANo Reference Image Quality Assessment Metric for 3D Synthesized Views [C]//IEEE International Conference on Acoustics.Washington,USA:IEEE,2017:1248-1252. [23] FARID M S,LUCENTEFORTE M,GRANGETTO M.Evaluating Virtual Image Quality using the Side-Views Information Fusion and Depth Maps [J].Information Fusion,2018,43:47-56. [24] YANG J C,SIM K,JIANG B,et al.No-reference stereoscopic image quality assessment based on hue summation-difference mapping image and binocular joint mutual filtering[J].Applied Optics,2018,57(14):3915-3926. |
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