Semantically Adaptive JND Modeling with Object-wise Feature Characterization and Cross-object Interaction

Abstract

This work proposed a spatio-temporal JND model based on semantic attention. Firstly, the principal semantic features affecting visual attention are extracted, including the semantic sensitivity, objective area and shape, central bias and contextual complexity, and the HVS responses of these four features are explored and quantified. Secondly, the semantic attention model is constructed by inscribing the attentional competition model, considering the interaction between different objects with limited perception resources. Finally, the obtained semantic attention weighting factor is combined with the basic spatial attention model to develop an improved transform domain JND model. Detailed performance results of different JND models are shown in Tab. 1. The simulation results validate that the proposed JND profile is highly consistent with HVS, with strong competitiveness among the state-of-the-art models.