A psychophysical performance-based approach to the quality assessment of image processing algorithms

Image processing algorithms are used to improve digital image representations in either their appearance or storage efficiency. The merit of these algorithms depends, in part, on visual perception by human observers. However, in practice, most are assessed numerically, and the perceptual metrics tha...

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Published in:	PloS one Vol. 17; no. 5; p. e0267056
Main Authors:	Baker, Daniel H., Summers, Robert J., Baldwin, Alex S., Meese, Tim S.
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 05.05.2022 Public Library of Science (PLoS)
Subjects:	Algorithms Biology and Life Sciences Cartesian coordinates Compression Digital imaging Engineering and Technology Experiments Filters Gabor filters Image processing Image quality Noise Noise reduction Parameters Physical Sciences Psychophysics Quality assessment Quality control Research and Analysis Methods Social Sciences Thresholds Visual observation Visual perception Visual perception driven algorithms White noise United Kingdom
ISSN:	1932-6203, 1932-6203
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Abstract	Image processing algorithms are used to improve digital image representations in either their appearance or storage efficiency. The merit of these algorithms depends, in part, on visual perception by human observers. However, in practice, most are assessed numerically, and the perceptual metrics that do exist are criterion sensitive with several shortcomings. Here we propose an objective performance-based perceptual measure of image quality and demonstrate this by comparing the efficacy of a denoising algorithm for a variety of filters. For baseline, we measured detection thresholds for a white noise signal added to one of a pair of natural images in a two-alternative forced-choice (2AFC) paradigm where each image was selected randomly from a set of n = 308 on each trial. In a series of experimental conditions, the stimulus image pairs were passed through various configurations of a denoising algorithm. The differences in noise detection thresholds with and without denoising are objective perceptual measures of the ability of the algorithm to render noise invisible. This was a factor of two (6dB) in our experiment and consistent across a range of filter bandwidths and types. We also found that thresholds in all conditions converged on a common value of PSNR, offering support for this metric. We discuss how the 2AFC approach might be used for other algorithms including compression, deblurring and edge-detection. Finally, we provide a derivation for our Cartesian-separable log-Gabor filters, with polar parameters. For the biological vision community this has some advantages over the more typical (i) polar-separable variety and (ii) Cartesian-separable variety with Cartesian parameters.
AbstractList	Image processing algorithms are used to improve digital image representations in either their appearance or storage efficiency. The merit of these algorithms depends, in part, on visual perception by human observers. However, in practice, most are assessed numerically, and the perceptual metrics that do exist are criterion sensitive with several shortcomings. Here we propose an objective performance-based perceptual measure of image quality and demonstrate this by comparing the efficacy of a denoising algorithm for a variety of filters. For baseline, we measured detection thresholds for a white noise signal added to one of a pair of natural images in a two-alternative forced-choice (2AFC) paradigm where each image was selected randomly from a set of n = 308 on each trial. In a series of experimental conditions, the stimulus image pairs were passed through various configurations of a denoising algorithm. The differences in noise detection thresholds with and without denoising are objective perceptual measures of the ability of the algorithm to render noise invisible. This was a factor of two (6dB) in our experiment and consistent across a range of filter bandwidths and types. We also found that thresholds in all conditions converged on a common value of PSNR, offering support for this metric. We discuss how the 2AFC approach might be used for other algorithms including compression, deblurring and edge-detection. Finally, we provide a derivation for our Cartesian-separable log-Gabor filters, with polar parameters. For the biological vision community this has some advantages over the more typical (i) polar-separable variety and (ii) Cartesian-separable variety with Cartesian parameters. Image processing algorithms are used to improve digital image representations in either their appearance or storage efficiency. The merit of these algorithms depends, in part, on visual perception by human observers. However, in practice, most are assessed numerically, and the perceptual metrics that do exist are criterion sensitive with several shortcomings. Here we propose an objective performance-based perceptual measure of image quality and demonstrate this by comparing the efficacy of a denoising algorithm for a variety of filters. For baseline, we measured detection thresholds for a white noise signal added to one of a pair of natural images in a two-alternative forced-choice (2AFC) paradigm where each image was selected randomly from a set of n = 308 on each trial. In a series of experimental conditions, the stimulus image pairs were passed through various configurations of a denoising algorithm. The differences in noise detection thresholds with and without denoising are objective perceptual measures of the ability of the algorithm to render noise invisible. This was a factor of two (6dB) in our experiment and consistent across a range of filter bandwidths and types. We also found that thresholds in all conditions converged on a common value of PSNR, offering support for this metric. We discuss how the 2AFC approach might be used for other algorithms including compression, deblurring and edge-detection. Finally, we provide a derivation for our Cartesian-separable log-Gabor filters, with polar parameters. For the biological vision community this has some advantages over the more typical (i) polar-separable variety and (ii) Cartesian-separable variety with Cartesian parameters. Image processing algorithms are used to improve digital image representations in either their appearance or storage efficiency. The merit of these algorithms depends, in part, on visual perception by human observers. However, in practice, most are assessed numerically, and the perceptual metrics that do exist are criterion sensitive with several shortcomings. Here we propose an objective performance-based perceptual measure of image quality and demonstrate this by comparing the efficacy of a denoising algorithm for a variety of filters. For baseline, we measured detection thresholds for a white noise signal added to one of a pair of natural images in a two-alternative forced-choice (2AFC) paradigm where each image was selected randomly from a set of n = 308 on each trial. In a series of experimental conditions, the stimulus image pairs were passed through various configurations of a denoising algorithm. The differences in noise detection thresholds with and without denoising are objective perceptual measures of the ability of the algorithm to render noise invisible. This was a factor of two (6dB) in our experiment and consistent across a range of filter bandwidths and types. We also found that thresholds in all conditions converged on a common value of PSNR, offering support for this metric. We discuss how the 2AFC approach might be used for other algorithms including compression, deblurring and edge-detection. Finally, we provide a derivation for our Cartesian-separable log-Gabor filters, with polar parameters. For the biological vision community this has some advantages over the more typical (i) polar-separable variety and (ii) Cartesian-separable variety with Cartesian parameters.Image processing algorithms are used to improve digital image representations in either their appearance or storage efficiency. The merit of these algorithms depends, in part, on visual perception by human observers. However, in practice, most are assessed numerically, and the perceptual metrics that do exist are criterion sensitive with several shortcomings. Here we propose an objective performance-based perceptual measure of image quality and demonstrate this by comparing the efficacy of a denoising algorithm for a variety of filters. For baseline, we measured detection thresholds for a white noise signal added to one of a pair of natural images in a two-alternative forced-choice (2AFC) paradigm where each image was selected randomly from a set of n = 308 on each trial. In a series of experimental conditions, the stimulus image pairs were passed through various configurations of a denoising algorithm. The differences in noise detection thresholds with and without denoising are objective perceptual measures of the ability of the algorithm to render noise invisible. This was a factor of two (6dB) in our experiment and consistent across a range of filter bandwidths and types. We also found that thresholds in all conditions converged on a common value of PSNR, offering support for this metric. We discuss how the 2AFC approach might be used for other algorithms including compression, deblurring and edge-detection. Finally, we provide a derivation for our Cartesian-separable log-Gabor filters, with polar parameters. For the biological vision community this has some advantages over the more typical (i) polar-separable variety and (ii) Cartesian-separable variety with Cartesian parameters.
Audience	Academic
Author	Baldwin, Alex S. Baker, Daniel H. Summers, Robert J. Meese, Tim S.
AuthorAffiliation	1 Department of Psychology and York Biomedical Research Institute, University of York, York, United Kingdom 3 Department of Ophthalmology & Visual Sciences, McGill Vision Research, McGill University, Montreal, Quebec, Canada Justus Liebig Universitat Giessen, GERMANY 2 College of Health & Life Sciences, Aston University, Birmingham, United Kingdom
AuthorAffiliation_xml	– name: 1 Department of Psychology and York Biomedical Research Institute, University of York, York, United Kingdom – name: 3 Department of Ophthalmology & Visual Sciences, McGill Vision Research, McGill University, Montreal, Quebec, Canada – name: 2 College of Health & Life Sciences, Aston University, Birmingham, United Kingdom – name: Justus Liebig Universitat Giessen, GERMANY
Author_xml	– sequence: 1 givenname: Daniel H. orcidid: 0000-0002-0161-443X surname: Baker fullname: Baker, Daniel H. – sequence: 2 givenname: Robert J. orcidid: 0000-0003-4857-7354 surname: Summers fullname: Summers, Robert J. – sequence: 3 givenname: Alex S. orcidid: 0000-0002-3830-6254 surname: Baldwin fullname: Baldwin, Alex S. – sequence: 4 givenname: Tim S. surname: Meese fullname: Meese, Tim S.
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Cites_doi	10.1109/ICIP.2016.7532932 10.5121/ijcsit.2011.3620 10.1016/S0042-6989(97)00124-7 10.1016/0042-6989(84)90065-8 10.1016/j.visres.2004.06.005 10.1098/rspb.2007.0957 10.2352/J.ImagingSci.Technol.2009.53.3.031105 10.1364/JOSAA.12.002573 10.1109/MSP.2008.930649 10.1088/0954-898X_6_3_003 10.1098/rspb.1992.0110 10.1016/j.inffus.2019.09.003 10.1016/S0165-1684(98)00124-8 10.1111/j.2044-8317.1965.tb00689.x 10.1163/156856899X00210 10.1364/JOSAA.1.000226 10.1016/j.bspc.2021.102962 10.1109/TIP.2003.819861 10.1007/s11263-006-0026-8 10.1152/jn.1987.58.6.1233 10.1098/rsos.170285 10.1109/97.995823 10.1364/JOSAA.4.002379 10.1068/p2996 10.1167/11.14.14 10.1117/1.3267105 10.1109/PCS.2018.8456273 10.1109/ACSSC.1993.342451 10.1113/jphysiol.1969.sp008862 10.1117/1.1329896 10.1364/JOSAA.17.000028 10.1109/ICIP.1995.537667 10.1167/11.1.23 10.1016/S0167-2789(00)00174-3 10.1364/AO.26.000157 10.1167/10.8.14 10.1016/0042-6989(87)90016-2 10.1167/12.10.20
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SubjectTerms	Algorithms Biology and Life Sciences Cartesian coordinates Compression Digital imaging Engineering and Technology Experiments Filters Gabor filters Image processing Image quality Noise Noise reduction Parameters Physical Sciences Psychophysics Quality assessment Quality control Research and Analysis Methods Social Sciences Thresholds Visual observation Visual perception Visual perception driven algorithms White noise
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Title	A psychophysical performance-based approach to the quality assessment of image processing algorithms
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