Adaptive Block-Size Transform Based Just-Noticeable Difference Profile for Images

  • Authors:

  • Lin Ma;King N. Ngan

  • Affiliations:

  • Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR;Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR

  • Venue:
  • PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
  • Year:
  • 2009

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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脳8 discrete cosine transform (DCT) based JND model is firstly extended to 16脳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.