Rethinking and Conceptualizing Just Noticeable Difference Estimation by Residual Learning

The human visual system (HVS) cannot perceive the pixel intensity change below a certain threshold which is also known as the just noticeable difference (JND). Conventional JND prediction models mainly follow a two-step pipeline by first modeling the diverse masking effects based on the findings of the HVS and then fusing the results of different masking effect models into an overall JND map. However, due to the insufficient understanding of the HVS properties at the current stage, it is difficult to devise accurate computational models to characterize the complex masking effects. Moreover, the reasonability of the manually designed fusion schemes also lacks justification. In this work, we rethink the JND estimation problem from a fresh perspective by conceptualizing the JND as the difference map between the pristine image and its corresponding Critical Perceptual Lossless (CPL) counterpart. Building on this insight, we introduce a deep residual learning framework called ResJND to learn the discrepancies between the pristine image and its CPL counterpart, aiming to predict JND map implicitly. To support the training of our proposed ResJND model, we construct a dedicated CPL image dataset called CPL-Set which comprises a collection of pristine images and their corresponding CPL images selected by thorough subjective experiments. Comprehensive experiments have conclusively shown that our ResJND model excels at accurately predicting the JND map. Additionally, it demonstrates superior performance in associated applications, such as JND-guided noise injection, JND-guided image compression, and distortion visibility prediction. Codes are available at: https://github.com/Knife646/ResJND.