research-article

Performance evaluation of edge-directed interpolation methods for noise-free images

Authors:

Yaoqin XieAuthors Info & Claims

ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

Pages 268 - 272

https://doi.org/10.1145/2499788.2499859

Published: 17 August 2013 Publication History

Abstract

Many interpolation methods have been developed for high visual quality, but fail for preserving image structures. Edges carry heavy structural messages for visual tasks. Importance of edge preservation imposes edge-directed interpolation (EDI) methods a center of focus. How to measure edge-preserving ability has not been mentioned. In this paper, two metrics are proposed to measure the ability by edge-preserving ratio from accuracy and robustness. Performance of four edge-directed interpolation with two traditional methods are evaluated on two groups of standard images with other six commonly-used metrics. Experimental results show that EDI methods are better than traditional methods with highly improved edge-preserving ratio.

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Cited By

Karpov P(2022)Design of Low-Artifact Interpolation Kernels by Means of Computer AlgebraMathematics in Computer Science10.1007/s11786-022-00538-316:2-3Online publication date: 24-Sep-2022
https://doi.org/10.1007/s11786-022-00538-3
Intaniyom TThananporn WWoraratpanya K(2021)Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN2021 13th International Conference on Information Technology and Electrical Engineering (ICITEE)10.1109/ICITEE53064.2021.9611842(226-231)Online publication date: 14-Oct-2021
https://doi.org/10.1109/ICITEE53064.2021.9611842
Chen GZhao HPang CLi TPang C(2019)Image Scaling: How Hard Can it Be?IEEE Access10.1109/ACCESS.2019.29403537(129452-129465)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2940353
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Index Terms

Performance evaluation of edge-directed interpolation methods for noise-free images
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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cover image ACM Other conferences

ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

August 2013

419 pages

ISBN:9781450322522

DOI:10.1145/2499788

Conference Chair:
Tat-Seng Chua
National University of Singapore, Singapore
,
General Chairs:
Ke Lu
University of Chinese Academy of Sciences, China
,
Tao Mei
Microsoft Research Asia, China
,
Xindong Wu
Hefei University of Technology, China

Copyright © 2013 ACM.

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]

Sponsors

NSF of China: National Natural Science Foundation of China
University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2013

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Funding Sources

National Natural Science Foundation of China
Guangdong Innovative Research Team Program of China
Ministry of Science and Technology of the People's Republic of China

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ICIMCS '13

Sponsor:

NSF of China
University of Sciences & Technology, Hefei

ICIMCS '13: International Conference on Internet Multimedia Computing and Service

August 17 - 19, 2013

Huangshan, China

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ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;

Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

Karpov P(2022)Design of Low-Artifact Interpolation Kernels by Means of Computer AlgebraMathematics in Computer Science10.1007/s11786-022-00538-316:2-3Online publication date: 24-Sep-2022
https://doi.org/10.1007/s11786-022-00538-3
Intaniyom TThananporn WWoraratpanya K(2021)Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN2021 13th International Conference on Information Technology and Electrical Engineering (ICITEE)10.1109/ICITEE53064.2021.9611842(226-231)Online publication date: 14-Oct-2021
https://doi.org/10.1109/ICITEE53064.2021.9611842
Chen GZhao HPang CLi TPang C(2019)Image Scaling: How Hard Can it Be?IEEE Access10.1109/ACCESS.2019.29403537(129452-129465)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2940353
Li YWang ZDai GWu SYu SXie Y(2017)Evaluation of realistic blurring image quality by using a shallow convolutional neural network2017 IEEE International Conference on Information and Automation (ICIA)10.1109/ICInfA.2017.8079022(853-857)Online publication date: Jul-2017
https://doi.org/10.1109/ICInfA.2017.8079022
Dai GWang ZLi YChen QYu SXie Y(2017)Evaluation of no-reference models to assess image sharpness2017 IEEE International Conference on Information and Automation (ICIA)10.1109/ICInfA.2017.8078993(683-687)Online publication date: Jul-2017
https://doi.org/10.1109/ICInfA.2017.8078993
Wu JLi WJeon G(2017)From coarse- to fine-grained implementation of edge-directed interpolation using a GPUInformation Sciences: an International Journal10.1016/j.ins.2017.01.002385:C(457-474)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.01.002
Safonov VKurilin VRychagov NTolstaya VSafonov IKurilin IRychagov MTolstaya E(2017)Image UpscalingAdaptive Image Processing Algorithms for Printing10.1007/978-981-10-6931-4_8(195-215)Online publication date: 2-Nov-2017
https://doi.org/10.1007/978-981-10-6931-4_8
Chen LJiang FZhang HWu SYu SXie Y(2016)Edge preservation ratio for image sharpness assessment2016 12th World Congress on Intelligent Control and Automation (WCICA)10.1109/WCICA.2016.7578241(1377-1381)Online publication date: Jun-2016
https://doi.org/10.1109/WCICA.2016.7578241
Liu YLuo BZhang ZZhu YWu SXie Y(2016)Weighting Wiener and total variation for image denoising2016 IEEE International Conference on Information and Automation (ICIA)10.1109/ICInfA.2016.7832052(1479-1483)Online publication date: Aug-2016
https://doi.org/10.1109/ICInfA.2016.7832052
Li WWu JShi J(2014)Fine-grained parallel implementation of edge-directed Image Interpolation on GPU2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS)10.1109/PADSW.2014.7097912(937-940)Online publication date: Dec-2014
https://doi.org/10.1109/PADSW.2014.7097912
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