No-reference quality assessment of tone mapped High Dynamic Range (HDR) images using transfer learning

We present a transfer learning framework for no-reference image quality assessment (NRIQA) of tonemapped High Dynamic Range (HDR) images. This work is motivated by the observation that quality assessment databases in general, and HDR image databases in particular are 'small' relative to the typical requirements for training deep neural networks. Transfer learning based approaches have been successful in such scenarios where learning from a related but larger database is transferred to the smaller database. Specifically, we propose a framework where the successful AlexNet is used to extract image features. This is followed by the application of Principal Component Analysis (PCA) to reduce the dimensionality of the feature vector (from 4096 to 400), given the small database size. A linear regression model is then fit to Mean Opinion Scores (MOS) using L2 regularization to prevent overfitting. We demonstrate state-of-the-art performance of the proposed approach on the ESPL-LIVE database.