基于特征域奇异值分解的图像质量评价

崔力; 浩明

基于特征域奇异值分解的图像质量评价

崔力¹,
浩明²

1.
西北工业大学电子信息学院, 西安 710072
2. 西安邮电大学通信与信息工程学院, 西安 710121

基金项目: 国家自然科学基金资助项目（61103062）；人事部留学人员科技活动择优资助项目；教育部留学回国人员科研启动基金资助项目

详细信息

中图分类号: TN911.73
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- 被引次数: 0
出版历程
- 收稿日期: 2013-01-15
- 网络出版日期: 2013-12-30

Image quality assessment based on singular value decomposition in multiple feature domains

Cui Li¹,
Hao Ming²

1.
School of Electronic and Information, Northwestern Polytechnic University, Xi'an 710072, China
2. School of Communication and Information Engineering, Xi'an University of Posts and Telecommunication, Xi'an 710121, China

摘要

摘要: 为了克服传统图像质量评价算法泛化能力不足的问题，提出一种基于特征域奇异值分解的图像质量预测模型.首先从多个特征域（图像及其梯度和相位一致性）中分别比较图像局部的奇异向量和奇异值差异完成视觉特征提取，随后利用支持向量机完成图像感知质量预测.实验表明：所提出的基于支持向量机而构建图像质量预测模型不仅在单个图像数据库上的表现要优于传统的图像质量评价算法，而且有着良好的跨数据库性能变现，表现出较高的泛化性；通过用集成学习器取代单个支持向量机，图像感知质量预测模型的泛化能力还可以进一步提高.
- 人眼视觉系统 /
- 奇异值分解 /
- 支持向量机
Abstract: To solve the insufficient generalization ability of the traditional image quality assessment (IQA) algorithms, an image quality predication (IQP) model based on the singular value decomposition in multiple feature domains was proposed. The visual features were extracted by comparing the difference of singular values and singular vectors between the corresponding local neighborhoods of reference and test images in the multiple feature domains (images and their gradient and phase congruency maps), and then fed into a support vector machine (SVM) to predict the perceptual quality of images. Subsequent experiments show that, proposed IQP model built on the SVM not only has a better performance than the traditional IQA algorithms on individual image databases, but also exhibits good generalization ability by having a good across-image-database performance. By replacing the SVM with an ensemble learner, the generalization ability of the proposed IQP model can be improved further.
- human visual system /
- singular value decomposition /
- support vector machine

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参考文献(21)

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