Image Quality Assessment by Quantifying Discrepancies of Multifractal Spectrums

Hang ZHANG; Yong DING; Peng Wei WU; Xue Tong BAI; Kai HUANG

doi:10.1587/transinf.2014EDP7036

IEICE TRANSACTIONS on Information

Image Quality Assessment by Quantifying Discrepancies of Multifractal Spectrums

Hang ZHANG, Yong DING, Peng Wei WU, Xue Tong BAI, Kai HUANG

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Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.9 pp.2453-2460

Publication Date: 2014/09/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2014EDP7036

Type of Manuscript: PAPER

Category: Image Processing and Video Processing

Authors

Hang ZHANG
  Zhejiang University
Yong DING
  Zhejiang University
Peng Wei WU
  Zhejiang University
Xue Tong BAI
  Zhejiang University
Kai HUANG
  Zhejiang University

Keyword

image quality assessment, multifractal spectrums, statistical complexity feature, human visual system

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Hang ZHANG, Yong DING, Peng Wei WU, Xue Tong BAI, Kai HUANG, "Image Quality Assessment by Quantifying Discrepancies of Multifractal Spectrums" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 9, pp. 2453-2460, September 2014, doi: 10.1587/transinf.2014EDP7036.
Abstract: Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7036/_p

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@ARTICLE{e97-d_9_2453,
author={Hang ZHANG, Yong DING, Peng Wei WU, Xue Tong BAI, Kai HUANG, },
journal={IEICE TRANSACTIONS on Information},
title={Image Quality Assessment by Quantifying Discrepancies of Multifractal Spectrums},
year={2014},
volume={E97-D},
number={9},
pages={2453-2460},
abstract={Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2014EDP7036},
ISSN={1745-1361},
month={September},}

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TY - JOUR
TI - Image Quality Assessment by Quantifying Discrepancies of Multifractal Spectrums
T2 - IEICE TRANSACTIONS on Information
SP - 2453
EP - 2460
AU - Hang ZHANG
AU - Yong DING
AU - Peng Wei WU
AU - Xue Tong BAI
AU - Kai HUANG
PY - 2014
DO - 10.1587/transinf.2014EDP7036
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2014
AB - Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.
ER -

IEICE TRANSACTIONS on Information