research-article

Scalable Color Quantization for Task-centric Image Compression

Authors:

Jong Hwan KoAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 2s

Article No.: 82, Pages 1 - 18

https://doi.org/10.1145/3551389

Published: 17 February 2023 Publication History

Abstract

Conventional image compression techniques targeted for the perceptual quality are not generally optimized for classification tasks using deep neural networks (DNNs). To compress images for DNN inference tasks, recent studies have proposed task-centric image compression methods with quantization techniques optimized for DNN inference. Among them, color quantization was proposed to reduce the amount of data per pixel by limiting the number of distinct colors (color space) in an image. However, quantizing images into various color space sizes requires training and inference of multiple DNNs, each of which is dedicated to each color space. To overcome this limitation, we propose a scalable color quantization method, where images with variable color space sizes can be extracted from a master image generated by a single DNN model. This scalability is enabled by weighted color grouping that constructs a color palette using critical color components for the classification task. We also propose an adaptive training method that can jointly optimize images with various color-space sizes. The results show that the proposed method supports dynamic changes of the color space size between 1–6 bit color space per pixel, while even increasing the inference accuracy at a low bit precision up to 20.2% and 46.6% compared to other task- and human-centric color quantizations, respectively.

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Cited By

Wenjin Wang Lu MDai XJiang P(2024)Financial Digital Images Compression Method Based on Discrete Cosine TransformAutomatic Control and Computer Sciences10.3103/S014641162470069X58:5(592-601)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.3103/S014641162470069X
Liu SLin WChen YZhang YDai WSee JXiong H(2024)A Unified Framework for Jointly Compressing Visual and Semantic DataACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365480020:7(1-24)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3654800
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
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Index Terms

Scalable Color Quantization for Task-centric Image Compression
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Appearance and texture representations
  2. Computer graphics
    1. Image compression

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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 19, Issue 2s

April 2023

545 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3572861

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 February 2023

Online AM: 01 August 2022

Accepted: 20 July 2022

Revised: 19 May 2022

Received: 21 May 2021

Published in TOMM Volume 19, Issue 2s

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Institute of Information and Communication Technology Planning Evaluation (IITP)
Information Technology Research Center (ITRC)
ICT Creative Consilience program
Artificial Intelligence Innovation Hub program

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Cited By

Wenjin Wang Lu MDai XJiang P(2024)Financial Digital Images Compression Method Based on Discrete Cosine TransformAutomatic Control and Computer Sciences10.3103/S014641162470069X58:5(592-601)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.3103/S014641162470069X
Liu SLin WChen YZhang YDai WSee JXiong H(2024)A Unified Framework for Jointly Compressing Visual and Semantic DataACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365480020:7(1-24)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3654800
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
Hou YGould SZheng L(2024)Scalable Deep Color Quantization: A Cluster Imitation ApproachIEEE Transactions on Image Processing10.1109/TIP.2024.341413233(5273-5283)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3414132
Wu KLi ZYang YLiu Q(2024)Deep video compression based on long-range temporal context learningComputer Vision and Image Understanding10.1016/j.cviu.2024.104127(104127)Online publication date: Aug-2024
https://doi.org/10.1016/j.cviu.2024.104127
Shi WTao FWen Y(2024)Joint super-resolution-based fast face image coding for human and machine visionThe Visual Computer10.1007/s00371-024-03428-wOnline publication date: 20-May-2024
https://doi.org/10.1007/s00371-024-03428-w
Celebi M(2023)Forty years of color quantization: a modern, algorithmic surveyArtificial Intelligence Review10.1007/s10462-023-10406-656:12(13953-14034)Online publication date: 27-Apr-2023
https://dl.acm.org/doi/10.1007/s10462-023-10406-6

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