Maximum likelihood difference scaling of image quality in compression-degraded images

Christophe Charrier; Laurence T. Maloney; Hocine Cherifi; Kenneth Knoblauch

doi:10.1364/JOSAA.24.003418

Journal of the Optical Society of America A
Vol. 24,
Issue 11,
pp. 3418-3426
(2007)
•https://doi.org/10.1364/JOSAA.24.003418

Maximum likelihood difference scaling of image quality in compression-degraded images

Christophe Charrier, Laurence T. Maloney, Hocine Cherifi, and Kenneth Knoblauch

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Christophe Charrier, Laurence T. Maloney, Hocine Cherifi, and Kenneth Knoblauch, "Maximum likelihood difference scaling of image quality in compression-degraded images," J. Opt. Soc. Am. A 24, 3418-3426 (2007)

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Table of Contents Category
- Vision, Color, and Visual Optics
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About this Article
History
- Original Manuscript: August 20, 2007
- Manuscript Accepted: August 20, 2007
- Published: October 1, 2007
Virtual Issues
Virtual Journal for Biomedical Optics Vol. 2, Iss. 12

Abstract

Lossy image compression techniques allow arbitrarily high compression rates but at the price of poor image quality. We applied maximum likelihood difference scaling to evaluate image quality of nine images, each compressed via vector quantization to ten different levels, within two different color spaces, RGB and CIE 1976 $L^{*} a^{*} b^{*}$ . In $L^{*} a^{*} b^{*}$ space, images could be compressed on average by 32% more than in RGB space, with little additional loss in quality. Further compression led to marked perceptual changes. Our approach permits a rapid, direct measurement of the consequences of image compression for human observers.

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Observer	Image	$a_{1}$	$a_{2}$	$a_{3}$	B	$a_{2} ∕ a_{1}$	β
CC	autumn	0.011	0.053	$- 0.328$	15.3	4.7	0.165
PYB	autumn	0.003	0.059	$- 0.440$	15.5	22.8	0.041
CC	clown	0.008	0.051	$- 0.264$	12.0	6.2	0.094
PYB	clown	0.007	0.058	$- 0.430$	15.5	8.2	0.077
CC	cloth	0.012	0.185	$- 2.313$	25.9	16.0	0.232
PYB	cloth	0.001	0.065	$- 0.553$	17.5	45.2	0.024
CC	detail	0.001	0.067	$- 0.532$	15.9	61.2	0.014
PYB	detail	0.006	0.080	$- 0.777$	20.2	12.9	0.095
CC	football	0.003	0.056	$- 0.353$	13.1	17.5	0.032
PYB	football	0.003	0.100	$- 1.021$	20.7	33.0	0.046
CC	lena	0.018	0.056	$- 0.381$	16.7	3.1	0.238
PYB	lena	0.011	0.080	$- 0.745$	20.3	7.1	0.180
CC	mandrill	0.014	0.057	$- 0.409$	16.3	4.2	0.166
PYB	mandrill	0.010	0.051	$- 0.194$	9.0	5.3	0.079
CC	apples	0.001	0.062	$- 0.429$	13.9	77.2	0.009
PYB	apples	0.001	0.072	$- 0.609$	17.0	65.8	0.014
CC	tahiti	0.000	0.063	$- 0.439$	14.0	443.3	0.002
PYB	tahiti	0.000	0.067	$- 0.513$	15.5	414.0	0.003

Observer	Image	$a_{1}$	$a_{2}$	$a_{3}$	B	$a_{2} ∕ a_{1}$	β
CC	autumn	0.049	0.017	0.225	17.4	0.4	0.798
PYB	autumn	0.047	0.001	0.453	22.3	0.0	0.992
CC	clown	0.007	0.046	$- 0.173$	8.6	6.4	0.055
PYB	clown	0.002	0.045	$- 0.190$	8.9	18.9	0.019
CC	cloth	0.000	0.059	$- 0.363$	12.4	416.2	0.002
PYB	cloth	0.000	0.052	$- 0.224$	8.7	109.5	0.004
CC	detail	0.021	0.051	$- 0.230$	13.7	2.5	0.262
PYB	detail	0.016	0.047	$- 0.166$	10.5	2.9	0.165
CC	football	0.000	0.059	$- 0.317$	10.9	182.6	0.003
PYB	football	0.002	0.075	$- 0.614$	16.5	49.8	0.018
CC	lena	0.001	0.054	$- 0.285$	10.7	88.9	0.006
PYB	lena	0.010	0.054	$- 0.284$	12.6	5.3	0.121
CC	mandrill	0.000	0.043	$- 0.144$	6.7	171.2	0.001
PYB	mandrill	0.009	0.044	$- 0.176$	9.9	4.7	0.089
CC	apples	0.004	0.068	$- 0.457$	14.2	16.5	0.053
PYB	apples	0.009	0.055	$- 0.385$	15.5	6.4	0.109
CC	tahiti	0.000	0.055	$- 0.318$	11.7	224.1	0.003
PYB	tahiti	$- 0.001$	0.060	$- 0.368$	12.9	$- 79.4$	0.012

Observer	Image	$a_{1}$	$a_{2}$	$a_{3}$	B	$a_{2} ∕ a_{1}$	β
CC	autumn	0.011	0.053	$- 0.328$	15.3	4.7	0.165
PYB	autumn	0.003	0.059	$- 0.440$	15.5	22.8	0.041
CC	clown	0.008	0.051	$- 0.264$	12.0	6.2	0.094
PYB	clown	0.007	0.058	$- 0.430$	15.5	8.2	0.077
CC	cloth	0.012	0.185	$- 2.313$	25.9	16.0	0.232
PYB	cloth	0.001	0.065	$- 0.553$	17.5	45.2	0.024
CC	detail	0.001	0.067	$- 0.532$	15.9	61.2	0.014
PYB	detail	0.006	0.080	$- 0.777$	20.2	12.9	0.095
CC	football	0.003	0.056	$- 0.353$	13.1	17.5	0.032
PYB	football	0.003	0.100	$- 1.021$	20.7	33.0	0.046
CC	lena	0.018	0.056	$- 0.381$	16.7	3.1	0.238
PYB	lena	0.011	0.080	$- 0.745$	20.3	7.1	0.180
CC	mandrill	0.014	0.057	$- 0.409$	16.3	4.2	0.166
PYB	mandrill	0.010	0.051	$- 0.194$	9.0	5.3	0.079
CC	apples	0.001	0.062	$- 0.429$	13.9	77.2	0.009
PYB	apples	0.001	0.072	$- 0.609$	17.0	65.8	0.014
CC	tahiti	0.000	0.063	$- 0.439$	14.0	443.3	0.002
PYB	tahiti	0.000	0.067	$- 0.513$	15.5	414.0	0.003

Observer	Image	$a_{1}$	$a_{2}$	$a_{3}$	B	$a_{2} ∕ a_{1}$	β
CC	autumn	0.049	0.017	0.225	17.4	0.4	0.798
PYB	autumn	0.047	0.001	0.453	22.3	0.0	0.992
CC	clown	0.007	0.046	$- 0.173$	8.6	6.4	0.055
PYB	clown	0.002	0.045	$- 0.190$	8.9	18.9	0.019
CC	cloth	0.000	0.059	$- 0.363$	12.4	416.2	0.002
PYB	cloth	0.000	0.052	$- 0.224$	8.7	109.5	0.004
CC	detail	0.021	0.051	$- 0.230$	13.7	2.5	0.262
PYB	detail	0.016	0.047	$- 0.166$	10.5	2.9	0.165
CC	football	0.000	0.059	$- 0.317$	10.9	182.6	0.003
PYB	football	0.002	0.075	$- 0.614$	16.5	49.8	0.018
CC	lena	0.001	0.054	$- 0.285$	10.7	88.9	0.006
PYB	lena	0.010	0.054	$- 0.284$	12.6	5.3	0.121
CC	mandrill	0.000	0.043	$- 0.144$	6.7	171.2	0.001
PYB	mandrill	0.009	0.044	$- 0.176$	9.9	4.7	0.089
CC	apples	0.004	0.068	$- 0.457$	14.2	16.5	0.053
PYB	apples	0.009	0.055	$- 0.385$	15.5	6.4	0.109
CC	tahiti	0.000	0.055	$- 0.318$	11.7	224.1	0.003
PYB	tahiti	$- 0.001$	0.060	$- 0.368$	12.9	$- 79.4$	0.012

Abstract

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Figures (6)

Tables (2)

Equations (17)

Journal of the Optical Society of America A