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How to Optimize the Utilization of Image Quality Metrics in Computer Vision?

Published: 27 March 2018 Publication History

Abstract

In this paper, we propose to show the importance to consider the image quality in Computer Vision (CV) applications. We also describe a proposed framework that not only take into account the quality but rather permits to select the more adapted measure for a given CV application. Here, the selection of the image quality metric is based on a degradation identification step using a Linear Discriminant Analysis (LDA) method. The proposed framework has been applied to a Full-Reference approach where the reference image is supposed to be available and for No-Reference approach where only the captured image is accessible. The method has been tested using the TID 2008 database, which is composed of 17 degradation types.

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Cited By

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  • (2022)Deep-Based Quality Assessment of Medical Images Through Domain Adaptation2022 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP46576.2022.9897600(3692-3696)Online publication date: 16-Oct-2022

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cover image ACM Other conferences
MedPRAI '18: Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence
March 2018
135 pages
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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  • IAPR: International Association for Pattern Recognition

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 March 2018

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Author Tags

  1. Computer Vision
  2. Degradation identification proceedings
  3. Image Quality

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  • (2022)Deep-Based Quality Assessment of Medical Images Through Domain Adaptation2022 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP46576.2022.9897600(3692-3696)Online publication date: 16-Oct-2022

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