research-article

Perceptual Image Compression with Block-Level Just Noticeable Difference Prediction

Authors:

C.-C. Jay KuoAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 16, Issue 4

Article No.: 126, Pages 1 - 15

https://doi.org/10.1145/3408320

Published: 28 January 2021 Publication History

Abstract

A block-level perceptual image compression framework is proposed in this work, including a block-level just noticeable difference (JND) prediction model and a preprocessing scheme. Specifically speaking, block-level JND values are first deduced by utilizing the OTSU method based on the variation of block-level structural similarity values between two adjacent picture-level JND values in the MCL-JCI dataset. After the JND value for each image block is generated, a convolutional neural network–based prediction model is designed to forecast block-level JND values for a given target image. Then, a preprocessing scheme is devised to modify the discrete cosine transform coefficients during JPEG compression on the basis of the distribution of block-level JND values of the target test image. Finally, the test image is compressed by the max JND value across all of its image blocks in the light of the initial quality factor setting. The experimental results demonstrate that the proposed block-level perceptual image compression method is able to achieve 16.75% bit saving as compared to the state-of-the-art method with similar subjective quality. The project page can be found at https://mic.tongji.edu.cn/43/3f/c9778a148287/page.htm.

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Index Terms

Perceptual Image Compression with Block-Level Just Noticeable Difference Prediction
1. Computing methodologies
  1. Computer graphics

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 16, Issue 4

November 2020

372 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3444749

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2021 ACM.

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Association for Computing Machinery

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Publication History

Published: 28 January 2021

Accepted: 01 May 2020

Received: 01 March 2020

Published in TOMM Volume 16, Issue 4

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National Natural Science Foundation of China
Ministry of Science and Technology of China
Shanghai Engineering Research Center of Industrial Vision Perception 8 Intelligent Computing

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https://dl.acm.org/doi/10.1145/3652518
Wang LShi YWang JChen SYin BLing N(2024)Graph Based Cross-Channel Transform for Color Image CompressionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363171020:4(1-25)Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1145/3631710
Nami SPakdaman FHashemi MShirmohammadi SGabbouj M(2024)Lightweight Multitask Learning for Robust JND Prediction Using Latent Space and Reconstructed FramesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.338998834:9(8657-8671)Online publication date: Sep-2024
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