Improved estimation for just-noticeable visual distortion

  • Authors:

  • X. H. Zhang;W. S. Lin;P. Xue

  • Affiliations:

  • School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;Institute for Infocomm Research, Singapore 119613, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore

  • Venue:
  • Signal Processing
  • Year:
  • 2005

Quantified Score

Hi-index 0.08

Visualization

Abstract

Perceptual visibility threshold estimation, based upon characteristics of the human visual system (HVS), has wide applications in digital image/video processing. An improved scheme for estimating just-noticeable distortion (JND) is proposed in this paper. It is proved to outperform the DCTune model, with the major contributions of a new formula for luminance adaptation adjustment and the incorporation of block classification for contrast masking. The HVS visibility threshold for digital images exhibits an approximately parabolic curve versus gray levels and this has been formulated to yield a more accurate base threshold. Moreover, edge regions have been differentiated via block classification to effectively avoid over-estimation of JND in the said regions. Experiments with different images and the associated subjective tests show improved performance of the proposed scheme over the DCTune model for luminance adaptation (especially in dark regions) and masking effect in edge regions. Our model has further demonstrated to achieve favorable results in perceptual visual distortion gauge and image compression. The improvement in JND estimation facilitates better visual distortion measurement and visual signal compression.