无参考模糊图像质量评价改进算法

针对传统无参考模糊图像质量评价算法存在高计算复杂度的问题，通过改进经典的二次模糊处理算法，提出一种快速有效的无参考模糊图像质量评价方法。该算法基于人眼视觉系统(HVS)特性，利用局部方差选取人眼感兴趣图像块代替整体图像，并将感兴趣图像块通过低通滤波处理，构造模糊图像块，通过计算滤波前后图像块相邻像素差值变化大小获取原始整体图像的客观质量评价参数。仿真测试结果表明，该算法与传统整体图像二次模糊算法相比，皮尔逊相关系数提高0.01，与主观评价结果更为一致；运算速度提高一倍，降低了运算复杂度。

Improved algorithm for no-reference quality assessment of blurred image

A fast and effective quality assessment algorithm of no-reference blurred image based on improving the classic Repeat blur (Reblur) processing algorithm was proposed for the high computational cost in traditional methods. The proposed algorithm took into account the human visual system, selected the image blocks that human was interested in instead of the entire image using the local variance, constructed blurred image blocks through low-pass filter, calculated the difference of the adjacent pixels between the original and the blurred image blocks to obtain the original image objective quality evaluation parameters. The simulation results show that compared to the traditional method, the proposed algorithm is more consistent with the subjective evaluation results with the Pearson correlation coefficient increasing 0.01 and less complex with half running time.