research-article

Lossless Image Set Compression Using Animated FLIF

Authors:

Takuya Sakurai,

Ushio InoueAuthors Info & Claims

ACIT '21: Proceedings of the the 8th International Virtual Conference on Applied Computing & Information Technology

Pages 99 - 104

https://doi.org/10.1145/3468081.3471130

Published: 22 October 2021 Publication History

Abstract

Many image datasets are available on the Internet, contributing to the development of computer vision. While huge datasets are useful for research, they are time-consuming to transfer due to their large data volume. In particular, lossless compression has a worse compression ratio than lossy compression. It is considered that a higher compression ratio can be achieved by encoding multiple images together exploiting the features in the dataset rather than encoding each image individually. In this paper, we propose a new method for efficient lossless compression of image sets by combining a minimum spanning tree (MST) and the Free Lossless Image Format (FLIF). The experimental results show that the compression ratio of the proposed method is better than that of the HEVC-based method. We also show that the compression ratio can be further improved by extending the entropy coder of FLIF, but the effect of the compression ratio improvement depends on the characteristics of the images in the set.

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Cited By

Nakashima KKawai YNakazawa RToda HAomori HOtake TMatsuda IItoh S(2023)Lifting-based lossless image coding using cellular neural network predictors and context estimators optimized by adaptive differential evolutionNonlinear Theory and Its Applications, IEICE10.1587/nolta.14.60914:3(609-627)Online publication date: 2023
https://doi.org/10.1587/nolta.14.609

Index Terms

Lossless Image Set Compression Using Animated FLIF
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image compression
2. Information systems
  1. Information systems applications
    1. Multimedia information systems

Index terms have been assigned to the content through auto-classification.

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ACIT '21: Proceedings of the the 8th International Virtual Conference on Applied Computing & Information Technology

June 2021

147 pages

ISBN:9781450384933

DOI:10.1145/3468081

Copyright © 2021 ACM.

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Publication History

Published: 22 October 2021

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ACIT 2021

ACIT 2021: The 8th International Virtual Conference on Applied Computing & Information Technology

June 20 - 22, 2021

Kanazawa, Japan

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Cited By

Nakashima KKawai YNakazawa RToda HAomori HOtake TMatsuda IItoh S(2023)Lifting-based lossless image coding using cellular neural network predictors and context estimators optimized by adaptive differential evolutionNonlinear Theory and Its Applications, IEICE10.1587/nolta.14.60914:3(609-627)Online publication date: 2023
https://doi.org/10.1587/nolta.14.609

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