Transferring From Distortion to Perception-Oriented Optimization: Just-Noticeable-Distortion-Based Domain Adaptation

The perception-distortion-tradeoff reveals the limitation of current low-level deep learning paradigms, i.e., minimizing reconstruction distortion does not guarantee improved perceptual quality. Acknowledging the lack of a reliable perception-oriented optimization function, we are motivated to explore a flexible approach for enhancing perceptual quality by steering the tradeoff to prioritize perception. To this end, we reconsider the perception-distortion function by incorporating the Just-Noticeable-Distortion (JND) mechanism. We mathematically demonstrate that in the common image restoration process, altering the optimization target from natural images to distorted images—where the distortion intensity is constrained by the JND threshold and the distortion type aligns with that arising from the restorer itself—effectively obtained improved perception indices without any changes to the restorer or optimization function. Accordingly, to facilitate various low-level learning models, we are motivated to construct the first large-scale CNN-oriented JND image dataset. Our dataset comprises 500 natural images and 4,500 degraded versions generated by a series of autoencoders, as well as the actual JND judgment results collected through rigorous subjective testing from twenty volunteers. Finally, a learning-based JND inference model is established on the proposed dataset and employed in the proposed JND-based adaptation scheme, where the inferred JND images serve as pseudo-ground truth for the training or fine-tuning processes of low-level vision models. Extensive experiments on image super-resolution and end-to-end image compression across multiple models have shown encouraging improvements in perceptual quality, demonstrating the effectiveness of the proposed scheme.