Image quality assessment method based on nonlinear feature extraction in kernel space

在实现对与人类视觉感知相一致的图像质量的客观评价中,如何提取图像的视觉感知特征至关重要。不同于传统方法中通过线性变换或模型表达图像的方式,本文采用高维空间的一种数学表达来揭示图像的统计特性,通过引入核独立分量分析(kernel independent component analysis,KICA)方法实现非线性转化和图像的高维特征提取。从而提出一种基于非线性特征提取的全参考图像质量评价方法。在LIVE、TID2008和CSIQ等图像质量评价数据库上的实验结果表明,图像的非线性特征更有利于图像内在质量的描述,并且本文提出的方法性能良好,与主观评价较为一致。

To match human perception, extracting perceptual features effectively plays an important role in image quality assessment. In contrast to most existing methods that use linear transformations or models to represent images, we employ a complex mathematical expression of high dimensionality to reveal the statistical characteristics of the images. Furthermore, by introducing kernel methods to transform the linear problem into a nonlinear one, a full-reference image quality assessment method is proposed based on high-dimensional nonlinear feature extraction. Experiments on the LIVE, TID2008, and CSIQ databases demonstrate that nonlinear features offer competitive performance for image inherent quality representation and the proposed method achieves a promising performance that is consistent with human subjective evaluation.