计算机科学与探索Issue(12)：1517-1524,8.DOI:10.3778/j.issn.1673-9418.1405036
多核学习纹理特征的无参考图像质量评价
Blind Image Quality Assessment with Texture Feature via Multiple Kernel Learning
摘要
Abstract
Because of the irregular features extracted from images with various types of distortion, the single kernel method cannot get the ideal result, this paper presents a non-reference image quality evaluation method based on multiple kernel learning for various learning types of distortion. Firstly this paper does a conversion of the gray scale image with the structure tensor and the phase congruency, then extracts the secondary statistical features of gray level-gradient co-occurrence matrix from them, finally inputs these features into hierarchical multiple kernel learning machine for training and gets the quality score. The random experiment results on multiple image library show that the new method results are consistent with the subjective evaluation values, and have better generalization.关键词
无参考图像质量评价/多核学习/灰度-梯度共生矩阵/结构张量/相位一致Key words
non-reference image quality assessment/multiple kernel learning/gray level-gradient co-occurrence matrix/structure tensor/phase congruency分类
信息技术与安全科学引用本文复制引用
严大卫,桑庆兵..多核学习纹理特征的无参考图像质量评价[J].计算机科学与探索,2014,(12):1517-1524,8.基金项目
The National Natural Science Foundation of China under Grant No.61170120(国家自然科学基金) （国家自然科学基金）
the Natural Science Foundation of Jiangsu Province of China under Grant No. BK2011147(江苏省自然科学基金) （江苏省自然科学基金）
the Prospective Research Project of Jiangsu Province under Grant No. BY2013015-41(江苏省产学研项目) （江苏省产学研项目）
