No-reference image contrast measure using image statistics and random forest

No-reference image contrast measure using image statistics and random forest Image quality assessment is very crucial for certain image processing applications. Images can be distorted from multiple sources like a fault in sensors, camera shake, poor lighting, over-exposure etc. All these distortions reduce the visual quality of images. Assessing the quality of images can be done with the use of a reference image of the same scene or without it. Without the use of reference image, quality assessment is a very difficult task. Machine learning approaches are very common in no-reference image quality assessment. No reference strategies are very useful if the type of distortion is known. Contrast assessment is a very important application in image processing as poor contrast images are difficult for automated image processing. In this paper, we propose a no-reference image quality measure for images with respect to contrast using random forest regression and validate the results using standard datasets. Experimental results on standard datasets show that the proposed method demonstrates promising results when compared to existing no-reference techniques and the proposed method shows high correlation values with human opinion scores. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

No-reference image contrast measure using image statistics and random forest

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-016-4335-9
Publisher site
See Article on Publisher Site

Abstract

Image quality assessment is very crucial for certain image processing applications. Images can be distorted from multiple sources like a fault in sensors, camera shake, poor lighting, over-exposure etc. All these distortions reduce the visual quality of images. Assessing the quality of images can be done with the use of a reference image of the same scene or without it. Without the use of reference image, quality assessment is a very difficult task. Machine learning approaches are very common in no-reference image quality assessment. No reference strategies are very useful if the type of distortion is known. Contrast assessment is a very important application in image processing as poor contrast images are difficult for automated image processing. In this paper, we propose a no-reference image quality measure for images with respect to contrast using random forest regression and validate the results using standard datasets. Experimental results on standard datasets show that the proposed method demonstrates promising results when compared to existing no-reference techniques and the proposed method shows high correlation values with human opinion scores.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jan 11, 2017

References

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