Image quality assessment using a neural network approach

In this paper, we propose a neural network approach to image quality assessment. In particular, the neural network measures the quality of an image by predicting the mean opinion score (MOS) of human observers, using a set of key features extracted from the original and test images. Experimental results, using 352 JPEG/JPEG2000 compressed images, show that the neural network outputs correlate highly with the MOS scores, and therefore, the neural network can easily serve as a correlate to subjective image quality assessment. Using 10-fold cross-validation, the predicted MOS values have a linear correlation coefficient of 0.9744, a Spearman ranked correlation of 0.9690, a mean absolute error of 3.75%, and an rms error of 4.77%. These results compare very favorably with the results obtained with other methods, such as the structural similarity index of Wang et al. [2004].