多核学习纹理特征的无参考图像质量评价-计算机科学与探索.pdfVIP

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 ，8（12）：1517-1524. ISSN 1673-9418 CODEN JKYTA8 E-mail: fcst@vip. 163.com Journal of Frontiers of Computer Science and Technology 1673-9418/2014/08(12)- 1517-08 Tel: +86-10 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 + 严大卫 ，桑庆兵 江南大学 物联网工程学院，江苏 无锡 214122 Blind Image Quality Assessment with Texture Feature via Multiple Kernel Learning + YAN Dawei , SANG Qingbing School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China + Corresponding author: E-mail: 229647702@ YAN Dawei, SANG Qingbing. Blind image quality assessment with texture feature via multiple kernel learning. Journal of Frontiers of Computer Science and Technology, 2014, 8(12) ：1517-1524. 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 bette