Just Noticeable Difference Level Prediction for Perceptual Image Compression

Tao TIAN, Hanli WANG, Lingxuan ZUO, C.-C. Jay KUO, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

21 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)690-700
Number of pages11
JournalIEEE Transactions on Broadcasting
Volume66
Issue number3
Early online date16 Mar 2020
DOIs
Publication statusPublished - Sept 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61622115 and Grant 61976159, and in part by the Shanghai Engineering Research Center of Industrial Vision Perception and Intelligent Computing under Grant 17DZ2251600. (Tao Tian and Hanli Wang are co-first authors.)

Keywords

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

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