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

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.

Key words: non-reference image quality assessment, multiple kernel learning, gray level-gradient co-occurrence matrix, structure tensor, phase congruency