Pre-training with fractal images facilitates learned image quality estimation

Today's image quality estimation is widely dominated by learning-based approaches. The availability of annotated, i.e. rated, images is often a bottleneck in training datadriven visual quality models and hinders their generalization power. This paper proposed a novel pre-training scheme for learning-based quality estimation that does not rely on human-annotated datasets, but leverages synthetic fractal images. These images can be synthesized inexhaustibly and are inherently labeled during generation. We evaluate the pre-training strategy on a popular neural network-based quality model and show that the training effort can be reduced significantly, resulting in better final accuracy and faster convergence speed.