摘要
立体图像质量评价是评价立体视频系统性能的有效途径,而模拟人类大脑神经网络进行特征提取是立体图像质量评价的关键.为此,提出一种基于极限学习机的全参考立体图像质量评价方法,包括3个阶段:1)对原始和失真立体图像分别进行特征描述,以左图像,右图像和独眼图作为输入信息,采用包含3个隐层的极限学习机将图像信息映射到特征空间,从而得到原始和失真立体图像的特征描述;2)对原始和失真立体图像的特征描述进行相似性度量,从而得到原始和失真立体图像的质量特征;3)采用极限学习机建立得到的12维质量特征与主观评价值的回归模型,并将训练得到的回归模型用于测试阶段,预测得到相应的客观质量评价值.实验结果表明,文中方法在对称和非对称立体图像数据库都取得了较好的性能,与人类的主观感知保持良好的一致性.
Stereoscopic image quality assessment(SIQA)has potential application in evaluating the performance of3D video system,while the key ingredient in SIQA is to extract visual features that can simulate human brain neural network.In this study,we propose a new full-reference SIQA method based on extreme learning machine(ELM).The proposed method mainly consists of three components.1)Feature representation for original and distorted stereoscopic images.Using left,right,and cyclopean images as inputs,by mapping the image information to feature space via three-layer ELM,we can obtain the feature representation.2)Quality vector between the original and distorted stereoscopic images is obtained by measuring the similarity of the feature representation between the original and distorted stereoscopic images at each layer.3)Based on the derived12-dimensional quality vectors and the corresponding subjective scores,a regression model is first trained via ELM,and the trained regression model is used to test the quality score at the test stage.Experimental results show that the proposed method is effective on both symmetrical and asymmetrical stereoscopic image databases,and can achieve high consistent alignment with subjective perception.
作者
沈力波
邵枫
蒋刚毅
郁梅
Shen Libo;Shao Feng;Jiang Gangyi;Yu Mei(Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2017年第5期791-798,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61271021)
关键词
立体图像
质量评价
独眼图
特征描述
极限学习机
stereoscopic image
quality assessment
cyclopean map
feature representation
extreme learning machine
