Image quality assessment using nonlinear learning methods

Objective image quality assessment plays an important role in various image processing applications, where the goal of this process is to automatically evaluate the image quality in agreement with human visual perception. In this paper, we propose three different nonlinear learning approaches in order to design image quality assessment models, which serve to predict the perceived image quality. The nonlinear learning approaches used for the aforementioned purpose are nonlinear regression, artificial neural network and regression tree. The largest publicly available image quality database TID2013 is used to benchmark and evaluate the prediction models. The image quality metrics, provided by this TID2013, are not independent and have the redundant information of image quality. This issue might have a negative impact on the training performance and cause overfitting. To avoid this problem and to simplify the model structure, we select the most significant image quality metrics, based on Pearson´s correlation measure and principal component analysis. Simulation results confirm that the three nonlinear learning models have high efficiency in predicting image quality. In addition, the regression tree model has low complexity and easy implementation, comparing to the two other prediction models.