Blind High Dynamic Range Quality estimation by disentangling perceptual and noise features in images
No reference image quality assessment(NR-IQA) on true High Dynamic Range (HDR) images is an unexplored and interesting research topic that has become relevant with the current boom in HDR technology. We first conduct a study on the performance of existing algorithms for NR-IQA, originally developed for Low Dynamic Range images(LDR), on HDR data. Using the results of our investigation, we then propose a new convolutional architecture for NR-IQA achieving state of the art performance in terms of correlation to human judgment on quality. The proposed model disentangles perceptual effects exhibited by the distorted image from the noise present in the image and is capable of extracting perceptually relevant features without the necessity of handcrafting the features. Our architecture predicts the amount and location of the visual errors present in the distorted images without the reference image and performs comparably to the state of the art Full Reference Image Quality assessment(FR-IQA) algorithms.