research-article

No-reference remote sensing image quality assessment based on the region of interest and structural similarity

Authors:

Di Liu,

Yingchun Li,

Shaojun ChenAuthors Info & Claims

ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing

Pages 64 - 67

https://doi.org/10.1145/3239576.3239586

Published: 16 June 2018 Publication History

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Abstract

Blur and noise are two common distortion factors which affect remote sensing image quality. And make it difficult to assess the remote sensing image quality. The Structure Similarity(SSIM) algorithm is simple, high efficient and accurate. However, it does not work well when there is cross distortion in the image. To deal with the problem of SSIM algorithm treating different regions of image identically, this paper considered the perceptual characteristics to different content and masking effect. The proposed method is the perceptual weighting used in the region of interest and based on SSIM algorithm. The experiment shows that, compared with the Peak Signal-Noise Rate(PSNR) index, the proposed index has good consistence with the Structure Similarity(SSIM) index, and can make an effective and correct evaluation of image with both noise and blur. This is an accurate and reliable no-reference remote sensing image quality assessment mothed, which is easy to implement.

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Cited By

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Ieremeiev OLukin VOkarma KEgiazarian K(2020)Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing ImagesRemote Sensing10.3390/rs1215234912:15(2349)Online publication date: 22-Jul-2020
https://doi.org/10.3390/rs12152349
Zhang CWang QLi X(2019)A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality AssessmentIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS.2019.8898659(10055-10058)Online publication date: Jul-2019
https://doi.org/10.1109/IGARSS.2019.8898659

Index Terms

No-reference remote sensing image quality assessment based on the region of interest and structural similarity
1. Information systems
  1. Information systems applications
    1. Mobile information processing systems

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Published In

ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing

June 2018

261 pages

ISBN:9781450364607

DOI:10.1145/3239576

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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University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

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Published: 16 June 2018

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ICAIP '18: 2018 the 2nd International Conference on Advances in Image Processing

June 16 - 18, 2018

Chengdu, China

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Ieremeiev OLukin VOkarma KEgiazarian K(2020)Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing ImagesRemote Sensing10.3390/rs1215234912:15(2349)Online publication date: 22-Jul-2020
https://doi.org/10.3390/rs12152349
Zhang CWang QLi X(2019)A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality AssessmentIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS.2019.8898659(10055-10058)Online publication date: Jul-2019
https://doi.org/10.1109/IGARSS.2019.8898659

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Content-partitioned structural similarity index for image quality assessment

Quality assessment on remote sensing image based on neural networks

A Gradient Weighted Structural Similarity Metric for Image Quality Assessment

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Content-partitioned structural similarity index for image quality assessment

Quality assessment on remote sensing image based on neural networks

A Gradient Weighted Structural Similarity Metric for Image Quality Assessment

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