Investigating Normalization Methods for CNN-Based Image Quality Assessment

Prior to training convolutional neural networks (CNNs) for image quality assessment (IQA), input normalization is sometimes recommended and sometimes not, according to the literature. Although input normalization is known to improve model training and helps in learning important features, it may result in the loss of information such as contrast, color, and luminance. To better explore this issue, we conduct an empirical study to first investigate the effect of normalization on model performance and then which normalization method best fits IQA among existing methods. The performances of the selected methods are statistically compared with three basic scaling methods. The application of normalization is found to be statistically significant on three IQA databases. The performance improvement on the overall databases, as well as per-individual degradation, is demonstrated in the experimental results.