Abstract
In this paper, we propose a novel adaptive block-size transform (ABT) based just-noticeable difference (JND) model for images. As different transform sizes have different properties on energy compaction and detailed information preservation, traditional 8×8 discrete cosine transform (DCT) based JND model is firstly extended to 16×16 DCT based JND model, by considering spatial contrast sensitivity function (CSF), the luminance adaptation effect and the contrast masking effect based on block classification. Furthermore, in order to obtain a more accurate JND profile, a novel strategy is proposed for deciding which size of transform is employed to generate the resulting JND model. And experimental results have demonstrated that the proposed model is consistent with human visual system (HVS). Compared with other JND profiles, our proposed model could tolerate more distortions and have much better perceptual quality.
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References
Weber’s Law of Just Noticeable Differences, http://www.usd.edu/psyc301/WebersLaw.htm
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Ma, L., Ngan, K.N. (2009). Adaptive Block-Size Transform Based Just-Noticeable Difference Profile for Images. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_122
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DOI: https://doi.org/10.1007/978-3-642-10467-1_122
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10466-4
Online ISBN: 978-3-642-10467-1
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