受二维差分脉冲编码调制(DPCM)影像压缩编码技术的启发，为了更好地运行影像质量评价，从影像像素相关性的角度探讨了一种基于灰度预测误差统计的影像质量评价方法。该方法首先利用线性预测算子和最小二乘估计来计算影像像素的灰度，然后统计整幅影像内的估计像素灰度值与对应像素点的实际灰度值的累计平均平方误差。该误差反映了影像像素间的相关性大小，其值越大，表明像素间的相关性越小，影像的反差与清晰度越好。试验结果表明，该方法对影像质量的变化敏感度明显高于传统的均方差、平均梯度和信息熵法，因而特别适合评估通过不同降噪方法处理的影像质量。不足的是该方法的计算效率不如传统的方差和信息熵方法快。

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.