| Abstract | Blind objective image quality assessment methods typically predict a single Mean Opinion Score (MOS) to represent perceived image quality. However, MOS discards valuable information about observer variability, as different score distributions can produce the same mean value. Predicting the full quality score distribution provides a richer and more realistic representation of perceptual quality. In this work, we propose an Image Quality Distribution Network (IQDN) in five configurations: a custom convolutional neural network trained from scratch and four transfer learning variants based on ResNet50, VGG16, Xception, and DenseNet121 backbones. The models were trained to predict five-bin normalized quality score histograms on three datasets: KonIQ-10k, CID2013, and a newly introduced NAP540 dataset. Performance was evaluated using multiple point-wise error and distribution similarity metrics. Results show that the Image Quality Distribution Network with Xception backbone consistently performs better than existing state-of-the-art approaches in terms of Earth Mover’s Distance, capturing score distributions more effectively. Furthermore, a hybrid loss function combining Kullback–Leibler (KL) divergence and Huber loss improved performance across datasets. The predicted distributions also reconstruct MOS that closely correlate with ground-truth MOS. Finally, incorporating a quality-aware backbone demonstrates strong potential, particularly in cross-dataset testing scenarios. |
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