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周俊明, 郁梅, 蒋刚毅, 彭宗举, 王旭, 王阿红. 利用奇异值分解法的立体图像客观质量评价模型[J]. 计算机辅助设计与图形学学报, 2011, 23(5): 870-877.
引用本文: 周俊明, 郁梅, 蒋刚毅, 彭宗举, 王旭, 王阿红. 利用奇异值分解法的立体图像客观质量评价模型[J]. 计算机辅助设计与图形学学报, 2011, 23(5): 870-877.
Zhou Junming, Yu Mei, Jiang Gangyi, Peng Zongju, Wang Xu, Wang Ahong. A Singular Value Decomposition Based Objective Quality Assessment Model on Stereoscopic Images[J]. Journal of Computer-Aided Design & Computer Graphics, 2011, 23(5): 870-877.
Citation: Zhou Junming, Yu Mei, Jiang Gangyi, Peng Zongju, Wang Xu, Wang Ahong. A Singular Value Decomposition Based Objective Quality Assessment Model on Stereoscopic Images[J]. Journal of Computer-Aided Design & Computer Graphics, 2011, 23(5): 870-877.

利用奇异值分解法的立体图像客观质量评价模型

A Singular Value Decomposition Based Objective Quality Assessment Model on Stereoscopic Images

  • 摘要: 为了利用人眼的立体掩蔽效应去除视频信号中存在的视觉心理冗余,以进一步提高立体视频编码效率,提出一种立体图像客观质量评价模型.首先通过对立体图像的主观视觉质量分析探寻具有统计意义的人眼立体掩蔽效应规律,为客观质量评价模型的建立和优化提供理论依据;然后根据图像的奇异值表征图像属性具有较强稳定性特点,并将其引入模型中;最后通过实验对该模型进行优化,并按照VQEG的质量评价参数对优化后的模型进行性能评价.实验结果表明,Pearson线性相关系数值为0.955,均方根误差值为3.737,Spearman等级相关系数值为0.906,异常值比率值为0.811%,表明文中提出的客观评价模型能够很好地预测人眼观看立体图像的主观感知.

     

    Abstract: Removing visual redundancy with binocular suppression in human visual system is an effective way to improve the encoding efficiency of stereoscopic video.Based on binocular suppression, an objective quality assessment model on stereoscopic images is proposed in this paper.By analyzing the visual quality of stereoscopic images, statistical features of binocular suppression are explored to establish and optimize the model.According to the singular values of the images with strong stability, singular value decomposition (SVD) is adopted to extract features of stereoscopic images in the model.Finally, a subjective study on the performance of the proposed model with VQEG standards is conducted, involving 370 images distorted by Gaussian blurring, white Gaussian noise, JPEG and JPEG2000 compressions, four performance indicators of the proposed model.Experimental results show that the Pearson's Correlation Coefficient (CC) value, the Root Mean Square Error (RMSE) value, the Spearman's Rank-Order Correlation Coefficient (SROCC) value, and the Outlier Ratio (OR) value are 0.955, 3.737, 0.906 and 0.811% respectively.The results indicate that the proposed model can predict human visual perception well.

     

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