title: Just Noticeable Difference Estimation for Screen Content Images.
abstract: We propose a novel just noticeable difference (JND) model for a screen content image (SCI). The distinct properties of the SCI result in different behaviors of the human visual system when viewing the textual content, which motivate us to employ a local parametric edge model with an adaptive representation of the edge profile in JND modeling. In particular, we decompose each edge profile into its luminance, contrast, and structure, and then evaluate the visibility threshold in different ways. The edge luminance adaptation, contrast masking, and structural distortion sensitivity are studied in subjective experiments, and the final JND model is established based on the edge profile reconstruction with tolerable variations. Extensive experiments are conducted to verify the proposed JND model, which confirm that it is accurate in predicting the JND profile, and outperforms the state-of-the-art schemes in terms of the distortion masking ability. Furthermore, we explore the applicability of the proposed JND model in the scenario of perceptually lossless SCI compression, and experimental results show that the proposed scheme can outperform the conventional JND guided compression schemes by providing better visual quality at the same coding bits.
authors.forename: Shiqi
authors.initials: S
authors.lastname: Wang
authors.forename: Lin
authors.initials: L
authors.lastname: Ma
authors.forename: Yuming
authors.initials: Y
authors.lastname: Fang
authors.forename: Weisi
authors.initials: W
authors.lastname: Lin
authors.forename: Siwei
authors.initials: S
authors.lastname: Ma
authors.forename: Wen
authors.initials: W
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D044962:Data Compression; D004056:Differential Threshold; D006801:Humans; D014796:Visual Perception