An Image Quality Evaluation Method Based on Gray Prediction Error
一种基于灰度预测误差统计的影像质量评价方法

Enlightened the image compression technique based on 2 D Differential Pulse Code Modulation(DPCM), and taken the correlation between pixel and its neighbors into consideration, a new image quality evaluation method is discussed by adding up the gray errors between every pixel of image and its predictions in the paper. The basic principles are that each pixel of image is first predicted by using linear prediction operator and least square estimate technique and then the mean square root of the difference between pixels' gray and their predictions are added up. This difference reflects the relativity of pixels each other. The larger the difference is, the smaller the relativity of pixels is, and the better the contrast and the definition of image are. The experiment results show that the discussed method is first more sensitive to the change of image quality than traditional mean square error(MSE) method, average gradient method and information entropy method, and then more suitable to the quality evaluation of the images processed by the different denoise methods removing noise. The shortcoming of this method is but that its efficiency is slightly inferior to that of the traditional MSE and information entropy.