模拟视觉感知系统的无参考模糊图像质量评价

为了获得与人类视觉感知一致的图像质量评价方法, 本文提出一种模拟视觉感知系统的无参考模糊图像质量评价方法. 该方法通过比较不同模糊程度的图像特征的相似度来度量图像质量. 首先, 通过对待测图像进行人工模糊, 获得不同模糊程度的图像. 然后, 通过视网膜模型提取图像的细节信息. 接着, 采用奇异值分解用来获得图像的内部结构信息. 之后, 将待测图像与其它不同模糊度图像之间的细节相似度和奇异值相似度作为度量图像模糊度的特征向量. 最后, 将这些度量特征向量输入支持向量回归模型(SVR)进行训练, 获得最终的图像质量评估模型.在常用数据库上的实验结果表明, 该方法与人眼主观视觉感知的一致性优于比较方法.

Quality Assessment for No-Reference Blur Image by Simulating Human Visual Perception System

In order to obtain an assessment method for image quality that is consistent with the human visual perception system, this study proposed a no-reference assessment method for blur image quality by simulating the human visual perception system. The proposed method evaluates images of different blurriness by comparing the similarity of their characteristics. First, the test image is blurred by Gaussian functions to different degrees. Second, their detailed information is obtained through the retinal model. Third, singular values are decomposed to measure the intrinsic structures of images. Then, the similarities in details and singular values among the test image and its blurred images are calculated as the characteristic vectors for image blurriness, which are input into a Support Vector Regression (SVR) model for training to generate the proposed assessment method for image quality. Experimental results on benchmark databases show that the proposed method is more consistent with the subjective visual perception of human visual system than the comparison methods.