基于稀疏字典学习的立体图像质量评价
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摘要
本文提出了一种基于稀疏字典学习的双通道立体图像质量评价方法.其中,一个通道结合视觉注意机制得到初始立体显著图,用中央偏移和中心凹特性对其进行优化得到最终的显著图,然后,对其进行稀疏字典训练获得显著字典;另一个通道将参考立体图像对进行SIFT特征变换,然后,对其进行稀疏字典训练获得SIFT字典.在测试阶段,利用已训练字典对参考图像和失真图像进行稀疏编码获得稀疏系数,并定义稀疏系数相似度指标以衡量参考图像和失真图像之间的信息差异;最后将两个通道的质量分数进行加权得到立体图像质量的客观分数.实验在两个公开LIVE库上进行测试,实验结果表明,本文算法的评价结果与主观评分具有更好的一致性,更加符合人类视觉系统的感知.
In this paper,a dual-channel quality assessment method of stereoscopic images using sparse representation was proposed. For one channel,the initial 3 D salient map was obtained with visual attention mechanism,and the final salient map was generated through optimization by centre bias and the fovea property,then is used to train a salient dictionary. For another channel,a scale-invariant feature transform(SIFT) dictionary was trained by SIFT features extracted from reference images. At the testing stage,the trained dictionaries were used to get the sparse coefficient matrices for reference images and distorted images,and a sparse coefficient similarity index was defined to measure the information difference between them. Finally,the quality scores of the two channels were pooled to achieve the object score of the stereoscopic image. The performance of the proposed stereoscopic image quality evaluation metric was verified on two publicly available LIVE databases,and experimental results show that the proposed algorithm achieves high consistent alignment with subjective assessment,which satisfies the HVS better.
In this paper,a dual-channel quality assessment method of stereoscopic images using sparse representation was proposed. For one channel,the initial 3 D salient map was obtained with visual attention mechanism,and the final salient map was generated through optimization by centre bias and the fovea property,then is used to train a salient dictionary. For another channel,a scale-invariant feature transform(SIFT) dictionary was trained by SIFT features extracted from reference images. At the testing stage,the trained dictionaries were used to get the sparse coefficient matrices for reference images and distorted images,and a sparse coefficient similarity index was defined to measure the information difference between them. Finally,the quality scores of the two channels were pooled to achieve the object score of the stereoscopic image. The performance of the proposed stereoscopic image quality evaluation metric was verified on two publicly available LIVE databases,and experimental results show that the proposed algorithm achieves high consistent alignment with subjective assessment,which satisfies the HVS better.
引文
[1]Khan M S,Appina B,Channappayya S S,et al.Fullreference stereo image quality assessment using natural stereo scene statistics[J].IEEE Signal Processing Letters,2015,22(11):1985-1989.
[2]Karimi M,Nejati M,Soroushmehr S M R,et al.Blind stereo quality assessment based on learned features from binocular combined images[J].IEEE Transactions on Multimedia,2017,19(11):2475-2489.
[3]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,2014,25(9):1837-1842.Wang Guanghua,Li Sumei,Zhu Dan,et al.Application of extreme learning machine in objective stereoscopic image quality assessment[J].Journal of Optoelectronics·Laser,2014,25(9):1837-1842(in Chinese).
[4]LüY,Yu M,Jiang G,et al.No-reference stereoscopic image quality assessment using binocular self-similarity and deep neural network[J].Signal Processing Image Communication,2016,47:346-357.
[5]Olshausen B A,Field D J.Sparse coding of sensory inputs[J].Current Opinion in Neurobiology,2004,14(4):481-487.
[6]Reinagel P.How do visual neurons respond in the real world[J].Current Opinion in Neurobiology,2001,11(4):437-442.
[7]李柯蒙,邵枫,蒋刚毅,等.基于稀疏表示的立体图像客观质量评价方法[J].光电子·激光,2014,25(11):2227-2233.Li Kemeng,Shao Feng,Jiang Gangyi,et al.The method of evaluating the objective quality of stereoscopic images based on sparse representation[J].Journal of Optoelectronics·Laser,2014,25(11):2227-2233(in Chinese).
[8]Khan M S,Channappayya S S.Sparsity based stereoscopic image quality assessment[C]//2016,Asilomar Conference on Signals,Systems and Computers.Pacific Grove,USA,2016:1858-1862.
[9]Shao F,Li K,Lin W,et al.Full-reference quality assessment of stereoscopic images by learning binocular receptive field properties[J].IEEE Transactions on Image Processing,2015,24(10):2971-2983.
[10]Peng Q,Cheung Y M,You X,et al.A hybrid of local and global saliencies for detecting image salient region and appearance[J].IEEE Transactions on Systems Man&Cybernetics Systems,2017,47(1):86-97.
[11]Cheng M M,Mitra N J,Huang X,et al.Global contrast based salient region detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):569-582.
[12]王飞.基于上下文和背景的视觉显著性检测[D].大连:大连理工大学,2013.Wang Fei.Visual Saliency Detection Based on Context and Background[D].Dalian:Dalian University of Technology,2013(in Chinese).
[13]Tseng P H,Carmi R,Cameron I G,et al.Quantifying center bias of observers in free viewing of dynamic natural scenes[J].Journal of Vision,2009,9(7):1-16.
[14]Efron B,Hastie T,Johnstone I,et al.Least angle regression[J].The Annals of Statistics,2004,32(2):407-451.
[15]Sch?lkopf B,Platt J,Hofmann T.Efficient sparse coding algorithms[J].Proc of Nips,2007,19:801-808.
[16]Censor Y A,Zenios S A.Parallel Optimization:Theory,Algorithms and Applications[M].Oxford:Oxford University Press,1998.
[2]Karimi M,Nejati M,Soroushmehr S M R,et al.Blind stereo quality assessment based on learned features from binocular combined images[J].IEEE Transactions on Multimedia,2017,19(11):2475-2489.
[3]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,2014,25(9):1837-1842.Wang Guanghua,Li Sumei,Zhu Dan,et al.Application of extreme learning machine in objective stereoscopic image quality assessment[J].Journal of Optoelectronics·Laser,2014,25(9):1837-1842(in Chinese).
[4]LüY,Yu M,Jiang G,et al.No-reference stereoscopic image quality assessment using binocular self-similarity and deep neural network[J].Signal Processing Image Communication,2016,47:346-357.
[5]Olshausen B A,Field D J.Sparse coding of sensory inputs[J].Current Opinion in Neurobiology,2004,14(4):481-487.
[6]Reinagel P.How do visual neurons respond in the real world[J].Current Opinion in Neurobiology,2001,11(4):437-442.
[7]李柯蒙,邵枫,蒋刚毅,等.基于稀疏表示的立体图像客观质量评价方法[J].光电子·激光,2014,25(11):2227-2233.Li Kemeng,Shao Feng,Jiang Gangyi,et al.The method of evaluating the objective quality of stereoscopic images based on sparse representation[J].Journal of Optoelectronics·Laser,2014,25(11):2227-2233(in Chinese).
[8]Khan M S,Channappayya S S.Sparsity based stereoscopic image quality assessment[C]//2016,Asilomar Conference on Signals,Systems and Computers.Pacific Grove,USA,2016:1858-1862.
[9]Shao F,Li K,Lin W,et al.Full-reference quality assessment of stereoscopic images by learning binocular receptive field properties[J].IEEE Transactions on Image Processing,2015,24(10):2971-2983.
[10]Peng Q,Cheung Y M,You X,et al.A hybrid of local and global saliencies for detecting image salient region and appearance[J].IEEE Transactions on Systems Man&Cybernetics Systems,2017,47(1):86-97.
[11]Cheng M M,Mitra N J,Huang X,et al.Global contrast based salient region detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):569-582.
[12]王飞.基于上下文和背景的视觉显著性检测[D].大连:大连理工大学,2013.Wang Fei.Visual Saliency Detection Based on Context and Background[D].Dalian:Dalian University of Technology,2013(in Chinese).
[13]Tseng P H,Carmi R,Cameron I G,et al.Quantifying center bias of observers in free viewing of dynamic natural scenes[J].Journal of Vision,2009,9(7):1-16.
[14]Efron B,Hastie T,Johnstone I,et al.Least angle regression[J].The Annals of Statistics,2004,32(2):407-451.
[15]Sch?lkopf B,Platt J,Hofmann T.Efficient sparse coding algorithms[J].Proc of Nips,2007,19:801-808.
[16]Censor Y A,Zenios S A.Parallel Optimization:Theory,Algorithms and Applications[M].Oxford:Oxford University Press,1998.