Just Noticeable Difference Level Prediction for Perceptual Image Compression

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

22 Scopus Citations
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Author(s)

  • Tao Tian
  • Hanli Wang
  • Lingxuan Zuo
  • C.-C. Jay Kuo
  • Sam Kwong

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)690-700
Number of pages11
Journal / PublicationIEEE Transactions on Broadcasting
Volume66
Issue number3
Online published16 Mar 2020
Publication statusPublished - Sept 2020

Abstract

A perceptual image compression framework is proposed in this work, including an adaptive picture-level just noticeable difference (PJND) prediction model and a perceptual coding scheme. Specifically speaking, a convolutional neural network (CNN) model is designed with the existing subjective image database to predict the PJND label for a given image. Then, the support vector regression model is utilized to determine the number of PJND levels. After that, a just noticeable difference generation algorithm is developed to compute the corresponding quality factor for each PJND level. Moreover, an effective perceptual coding scheme is devised for perceptual image compression. Finally, the accuracy of the proposed PJND prediction model and the performance of the proposed perceptual coding scheme are evaluated. The experimental results show that the proposed CNN based PJND prediction model achieves good prediction accuracy and the proposed perceptual coding scheme produces state-of-the-art rate distortion performances.

Research Area(s)

  • convolutional neural network, Perceptual image compression, picture-level just noticeable difference

Citation Format(s)

Just Noticeable Difference Level Prediction for Perceptual Image Compression. / Tian, Tao; Wang, Hanli; Zuo, Lingxuan et al.
In: IEEE Transactions on Broadcasting, Vol. 66, No. 3, 09.2020, p. 690-700.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review