Abstract

Image quality assessment (IQA) enables distortions introduced into an image (e.g., through lossy compression or broadcast) to be measured and evaluated for severity. It is unclear to what degree affective image content may influence this process. In this study, participants (n=25) were found to be unable to disentangle affective image content from objective image quality in a standard IQA procedure (single stimulus numerical categorical scale). We propose that this issue is worthy of consideration, particularly in single stimulus IQA techniques, in which a small number of handpicked images, not necessarily representative of the gamut of affect seen in true broadcasting, and unrated for affective content, serve as stimuli.

© 2012 Optical Society of America

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2011 (4)

W. Lin and C.-C. Jay Kuo, “Perceptual quality metrics: a survey,” J. Visual Commun. Image Represent. 22, 297–312 (2011).
[CrossRef]

A. K. Moorthy and A. C. Bovik, “Visual quality assessment: what does the future hold?” Multimedia Tools Appl. 51, 675–696 (2011).
[CrossRef]

J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
[CrossRef]

E. S. Dan-Glauser and K. R. Scherer, “The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance,” Behav. Res. Meth. 43, 468–477 (2011).
[CrossRef]

2010 (3)

P. Kortum and M. Sullivan, “The effect of content desirability on subjective video quality ratings,” Hum. Factors 52, 105–118 (2010).
[CrossRef]

E. C. Larson and D. M. Chandler, “Most apparent distortion: full-reference image quality assessment and the role of strategy,” J. Electron. Imaging 19, 011006 (2010).
[CrossRef]

K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack, “Study of subjective and objective quality assessment of video,” IEEE Trans. Image Process. 19, 1427–1441 (2010).
[CrossRef]

2009 (3)

N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008—A database for evaluation of full-reference visual quality assessment metrics,” Adv. Mod. Radioelectron. 10, 30–45 (2009).

A. K. Moorthy and A. C. Bovik, “Visual importance pooling for image quality assessment,” IEEE J. Sel. Top. Signal Process. 3, 193–201 (2009).
[CrossRef]

C. Li and T. Chen, “Aesthetic visual quality assessment of paintings,” IEEE J. Sel. Top. Signal Process. 3, 236–252(2009).
[CrossRef]

2008 (1)

M. Veltkamp, H. Aarts, and R. Custers, “Perception in the service of goal pursuit: motivation to attain goals enhances the perceived size of goal-instrumental objects,” Soc. Cogn. 26, 720–736 (2008).
[CrossRef]

2007 (2)

D. M. Chandler and S. S. Hemami, “VSNR: a wavelet-based visual signal-to-noise ratio for natural images,” IEEE Trans. Image Process. 16, 2284–2298 (2007).
[CrossRef]

U. Rajashekar, I. van der Linde, A. C. Bovik, and L. K. Cormack, “Foveated analysis of image features at visual fixations,” Vis. Res. 47, 3160–3172 (2007).
[CrossRef]

2006 (1)

H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Trans. Image Process. 15, 430–444 (2006).
[CrossRef]

2004 (3)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error measurement to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

A. L. Barrowcliff, N. S. Gray, T. C. A. Freeman, and M. J. McCulloch, “Eye-movements reduce the vividness, emotional valence and electrodermal arousal associated with negative autobiographical memories,” J. Forens. Psychiatry Psychol. 15, 325–345 (2004).
[CrossRef]

S. Anders, M. Lotze, M. Erb, W. Grodd, and N. Birbaumer, “Brain activity underlying emotional valence and arousal: a response-related fMRI study,” Hum. Brain Mapp. 23, 200–209 (2004).
[CrossRef]

2002 (2)

A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
[CrossRef]

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

2001 (1)

G. Gorn, M. T. Pham, and L. Y. Sin, “When arousal influences ad evaluation and valence does not (and vice versa),” J. Consumer Psychol. 11, 43–55 (2001).
[CrossRef]

2000 (2)

A. E. Savakis, S. P. Etz, and A. C. Loui, “Evaluation of image appeal in consumer photography,” Proc. SPIE 3959, 111–120 (2000).
[CrossRef]

N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. Image Process. 9, 636–650 (2000).
[CrossRef]

1999 (3)

S. Winker, “Issues in vision modeling for perceptual video quality assessment,” Signal Process. 78, 231–252 (1999).
[CrossRef]

M. Bhalla and D. R. Proffitt, “Visual-motor recalibration in geographical slant perception,” J. Exp. Psychol. Hum. Percept. Perform. 25, 1076–1096 (1999).
[CrossRef]

B. N. Cuthbert, H. T. Schupp, M. M. Bradley, N. Burbaumer, and P. J. Lang, “Brain potentials in affective picture processing: covariation with autonomic arousal and affective report,” Biol. Psychol. 52, 95–111 (1999).
[CrossRef]

1995 (3)

A. L. Bouhuys, G. M. Bloem, and T. G. G. Groothius, “Induction of depressed and elated mood by music influences the perception of facial emotional expressions in healthy subjects,” J. Affective Disord. 33, 215–226 (1995).
[CrossRef]

A. M. Eskicioglu and P. S. Fisher, “Image quality measures and their performance,” IEEE Trans. Commun. 43, 2959–2965 (1995).
[CrossRef]

A. M. van Dijk, J.-B. Martens, and A. B. Watson, “Quality assessment of coded images using numerical category scaling,” Proc. SPIE 2451, 90–101 (1995).
[CrossRef]

1993 (1)

P. J. Lang, M. K. Greenwald, M. M. Bradley, and A. O. Hamm, “Looking at pictures: Affective, facial, visceral, and behavioral reactions,” Psychophysiology 30, 261–273 (1993).
[CrossRef]

1992 (2)

M. M. Bradley, M. K. Greenwald, M. C. Petry, and P. J. Lang, “Remembering pictures: pleasure and arousal in memory,” J. Exper. Psychol. Learn. Mem. Cogn. 18, 379–390 (1992).
[CrossRef]

J. Cohen, “A power primer,” Psychol. Bull. 112, 155–159 (1992).
[CrossRef]

1991 (1)

G. K. Wallace, “The JPEG still picture compression standard,” Commun. ACM 34, 30–44 (1991).
[CrossRef]

1990 (1)

H. de Ridder and G. M. Majoor, “Numerical category scaling: an efficient method for assessing digital image coding impairments,” Proc. SPIE 1249, 65 (1990).
[CrossRef]

1983 (1)

N. Schwarz and G. L. Clore, “Mood, misattribution, and judgments of well-bring: Informative and reactive functions of affective states,” J. Pers. Soc. Well-Being 45513–523 (1983).

1947 (1)

J. S. Bruner and C. C. Goodman, “Value and need as organizing factors in perception,” J. Abnormal Psych. 42, 33–44 (1947).
[CrossRef]

Aarts, H.

M. Veltkamp, H. Aarts, and R. Custers, “Perception in the service of goal pursuit: motivation to attain goals enhances the perceived size of goal-instrumental objects,” Soc. Cogn. 26, 720–736 (2008).
[CrossRef]

Anders, S.

S. Anders, M. Lotze, M. Erb, W. Grodd, and N. Birbaumer, “Brain activity underlying emotional valence and arousal: a response-related fMRI study,” Hum. Brain Mapp. 23, 200–209 (2004).
[CrossRef]

Autrusseau, F.

S. Tourancheau, F. Autrusseau, Z. M. P. Sazzad, and Y. Horita, “Impact of subjective dataset on the performance of image quality metrics,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp. 365–368.

Barrowcliff, A. L.

A. L. Barrowcliff, N. S. Gray, T. C. A. Freeman, and M. J. McCulloch, “Eye-movements reduce the vividness, emotional valence and electrodermal arousal associated with negative autobiographical memories,” J. Forens. Psychiatry Psychol. 15, 325–345 (2004).
[CrossRef]

Battisti, F.

N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008—A database for evaluation of full-reference visual quality assessment metrics,” Adv. Mod. Radioelectron. 10, 30–45 (2009).

Beerends, J. G.

A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
[CrossRef]

Bhalla, M.

M. Bhalla and D. R. Proffitt, “Visual-motor recalibration in geographical slant perception,” J. Exp. Psychol. Hum. Percept. Perform. 25, 1076–1096 (1999).
[CrossRef]

Birbaumer, N.

S. Anders, M. Lotze, M. Erb, W. Grodd, and N. Birbaumer, “Brain activity underlying emotional valence and arousal: a response-related fMRI study,” Hum. Brain Mapp. 23, 200–209 (2004).
[CrossRef]

Bloem, G. M.

A. L. Bouhuys, G. M. Bloem, and T. G. G. Groothius, “Induction of depressed and elated mood by music influences the perception of facial emotional expressions in healthy subjects,” J. Affective Disord. 33, 215–226 (1995).
[CrossRef]

Bouhuys, A. L.

A. L. Bouhuys, G. M. Bloem, and T. G. G. Groothius, “Induction of depressed and elated mood by music influences the perception of facial emotional expressions in healthy subjects,” J. Affective Disord. 33, 215–226 (1995).
[CrossRef]

Bovik, A. C.

A. K. Moorthy and A. C. Bovik, “Visual quality assessment: what does the future hold?” Multimedia Tools Appl. 51, 675–696 (2011).
[CrossRef]

K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack, “Study of subjective and objective quality assessment of video,” IEEE Trans. Image Process. 19, 1427–1441 (2010).
[CrossRef]

A. K. Moorthy and A. C. Bovik, “Visual importance pooling for image quality assessment,” IEEE J. Sel. Top. Signal Process. 3, 193–201 (2009).
[CrossRef]

U. Rajashekar, I. van der Linde, A. C. Bovik, and L. K. Cormack, “Foveated analysis of image features at visual fixations,” Vis. Res. 47, 3160–3172 (2007).
[CrossRef]

H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Trans. Image Process. 15, 430–444 (2006).
[CrossRef]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error measurement to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. Image Process. 9, 636–650 (2000).
[CrossRef]

Z. Wang, H. R. Sheikh, and A. C. Bovik, “Objective video quality assessment,” in The Handbook of Video Databases: Design and Applications, B. Furht and O. Marqure, eds. (CRC, 2003), pp 1041–1078.

K. Seshadrinathan and A. C. Bovik, “Video quality assessment,” in The Essential Guide to Video Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 14.

K. Seshadrinathan, R. J. Safranek, J. Chen, T. N. Pappas, H. R. Sheikh, E. P. Simoncelli, Z. Wang, and A. C. Bovik, “Image quality assessment,” in The Essential Guide to Image Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 20.

Bradley, M. M.

B. N. Cuthbert, H. T. Schupp, M. M. Bradley, N. Burbaumer, and P. J. Lang, “Brain potentials in affective picture processing: covariation with autonomic arousal and affective report,” Biol. Psychol. 52, 95–111 (1999).
[CrossRef]

P. J. Lang, M. K. Greenwald, M. M. Bradley, and A. O. Hamm, “Looking at pictures: Affective, facial, visceral, and behavioral reactions,” Psychophysiology 30, 261–273 (1993).
[CrossRef]

M. M. Bradley, M. K. Greenwald, M. C. Petry, and P. J. Lang, “Remembering pictures: pleasure and arousal in memory,” J. Exper. Psychol. Learn. Mem. Cogn. 18, 379–390 (1992).
[CrossRef]

P. J. Lang, M. M. Bradley, and B. N. Cuthbert, “International affective picture system (IAPS): instruction manual and affective ratings,” Technical Report A-4 (The Center for Research in Psychophysiology, University of Florida, 1999).

Bruner, J. S.

J. S. Bruner and C. C. Goodman, “Value and need as organizing factors in perception,” J. Abnormal Psych. 42, 33–44 (1947).
[CrossRef]

Burbaumer, N.

B. N. Cuthbert, H. T. Schupp, M. M. Bradley, N. Burbaumer, and P. J. Lang, “Brain potentials in affective picture processing: covariation with autonomic arousal and affective report,” Biol. Psychol. 52, 95–111 (1999).
[CrossRef]

Carli, M.

N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008—A database for evaluation of full-reference visual quality assessment metrics,” Adv. Mod. Radioelectron. 10, 30–45 (2009).

Chandler, D. M.

E. C. Larson and D. M. Chandler, “Most apparent distortion: full-reference image quality assessment and the role of strategy,” J. Electron. Imaging 19, 011006 (2010).
[CrossRef]

D. M. Chandler and S. S. Hemami, “VSNR: a wavelet-based visual signal-to-noise ratio for natural images,” IEEE Trans. Image Process. 16, 2284–2298 (2007).
[CrossRef]

Chen, J.

K. Seshadrinathan, R. J. Safranek, J. Chen, T. N. Pappas, H. R. Sheikh, E. P. Simoncelli, Z. Wang, and A. C. Bovik, “Image quality assessment,” in The Essential Guide to Image Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 20.

Chen, N.

J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
[CrossRef]

Chen, T.

C. Li and T. Chen, “Aesthetic visual quality assessment of paintings,” IEEE J. Sel. Top. Signal Process. 3, 236–252(2009).
[CrossRef]

Clore, G. L.

N. Schwarz and G. L. Clore, “Mood, misattribution, and judgments of well-bring: Informative and reactive functions of affective states,” J. Pers. Soc. Well-Being 45513–523 (1983).

Cohen, J.

J. Cohen, “A power primer,” Psychol. Bull. 112, 155–159 (1992).
[CrossRef]

Cormack, L. K.

K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack, “Study of subjective and objective quality assessment of video,” IEEE Trans. Image Process. 19, 1427–1441 (2010).
[CrossRef]

U. Rajashekar, I. van der Linde, A. C. Bovik, and L. K. Cormack, “Foveated analysis of image features at visual fixations,” Vis. Res. 47, 3160–3172 (2007).
[CrossRef]

Custers, R.

M. Veltkamp, H. Aarts, and R. Custers, “Perception in the service of goal pursuit: motivation to attain goals enhances the perceived size of goal-instrumental objects,” Soc. Cogn. 26, 720–736 (2008).
[CrossRef]

Cuthbert, B. N.

B. N. Cuthbert, H. T. Schupp, M. M. Bradley, N. Burbaumer, and P. J. Lang, “Brain potentials in affective picture processing: covariation with autonomic arousal and affective report,” Biol. Psychol. 52, 95–111 (1999).
[CrossRef]

P. J. Lang, M. M. Bradley, and B. N. Cuthbert, “International affective picture system (IAPS): instruction manual and affective ratings,” Technical Report A-4 (The Center for Research in Psychophysiology, University of Florida, 1999).

Damera-Venkata, N.

N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. Image Process. 9, 636–650 (2000).
[CrossRef]

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A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
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H. de Ridder and G. M. Majoor, “Numerical category scaling: an efficient method for assessing digital image coding impairments,” Proc. SPIE 1249, 65 (1990).
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N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008—A database for evaluation of full-reference visual quality assessment metrics,” Adv. Mod. Radioelectron. 10, 30–45 (2009).

Ehrsam, M.

A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
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A. M. Eskicioglu and P. S. Fisher, “Image quality measures and their performance,” IEEE Trans. Commun. 43, 2959–2965 (1995).
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A. E. Savakis, S. P. Etz, and A. C. Loui, “Evaluation of image appeal in consumer photography,” Proc. SPIE 3959, 111–120 (2000).
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N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. Image Process. 9, 636–650 (2000).
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A. M. Eskicioglu and P. S. Fisher, “Image quality measures and their performance,” IEEE Trans. Commun. 43, 2959–2965 (1995).
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A. L. Barrowcliff, N. S. Gray, T. C. A. Freeman, and M. J. McCulloch, “Eye-movements reduce the vividness, emotional valence and electrodermal arousal associated with negative autobiographical memories,” J. Forens. Psychiatry Psychol. 15, 325–345 (2004).
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N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. Image Process. 9, 636–650 (2000).
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J. S. Bruner and C. C. Goodman, “Value and need as organizing factors in perception,” J. Abnormal Psych. 42, 33–44 (1947).
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G. Gorn, M. T. Pham, and L. Y. Sin, “When arousal influences ad evaluation and valence does not (and vice versa),” J. Consumer Psychol. 11, 43–55 (2001).
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A. L. Barrowcliff, N. S. Gray, T. C. A. Freeman, and M. J. McCulloch, “Eye-movements reduce the vividness, emotional valence and electrodermal arousal associated with negative autobiographical memories,” J. Forens. Psychiatry Psychol. 15, 325–345 (2004).
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A. L. Bouhuys, G. M. Bloem, and T. G. G. Groothius, “Induction of depressed and elated mood by music influences the perception of facial emotional expressions in healthy subjects,” J. Affective Disord. 33, 215–226 (1995).
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P. J. Lang, M. K. Greenwald, M. M. Bradley, and A. O. Hamm, “Looking at pictures: Affective, facial, visceral, and behavioral reactions,” Psychophysiology 30, 261–273 (1993).
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A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
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D. M. Chandler and S. S. Hemami, “VSNR: a wavelet-based visual signal-to-noise ratio for natural images,” IEEE Trans. Image Process. 16, 2284–2298 (2007).
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S. Tourancheau, F. Autrusseau, Z. M. P. Sazzad, and Y. Horita, “Impact of subjective dataset on the performance of image quality metrics,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp. 365–368.

Hu, X.

J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
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W. Lin and C.-C. Jay Kuo, “Perceptual quality metrics: a survey,” J. Visual Commun. Image Represent. 22, 297–312 (2011).
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J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
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J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
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N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. Image Process. 9, 636–650 (2000).
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Koenen, R. H.

A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
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Kohler, S.

A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
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P. Kortum and M. Sullivan, “The effect of content desirability on subjective video quality ratings,” Hum. Factors 52, 105–118 (2010).
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B. N. Cuthbert, H. T. Schupp, M. M. Bradley, N. Burbaumer, and P. J. Lang, “Brain potentials in affective picture processing: covariation with autonomic arousal and affective report,” Biol. Psychol. 52, 95–111 (1999).
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P. J. Lang, M. K. Greenwald, M. M. Bradley, and A. O. Hamm, “Looking at pictures: Affective, facial, visceral, and behavioral reactions,” Psychophysiology 30, 261–273 (1993).
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M. M. Bradley, M. K. Greenwald, M. C. Petry, and P. J. Lang, “Remembering pictures: pleasure and arousal in memory,” J. Exper. Psychol. Learn. Mem. Cogn. 18, 379–390 (1992).
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A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
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Li, C.

C. Li and T. Chen, “Aesthetic visual quality assessment of paintings,” IEEE J. Sel. Top. Signal Process. 3, 236–252(2009).
[CrossRef]

Li, J.

R. Datta, J. Li, and J. Z. Wang, “Algorithmic inferencing of aesthetics and emotion in natural images: an exposition,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp 105–108.

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W. Lin and C.-C. Jay Kuo, “Perceptual quality metrics: a survey,” J. Visual Commun. Image Represent. 22, 297–312 (2011).
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S. Anders, M. Lotze, M. Erb, W. Grodd, and N. Birbaumer, “Brain activity underlying emotional valence and arousal: a response-related fMRI study,” Hum. Brain Mapp. 23, 200–209 (2004).
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Loui, A. C.

A. E. Savakis, S. P. Etz, and A. C. Loui, “Evaluation of image appeal in consumer photography,” Proc. SPIE 3959, 111–120 (2000).
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Lukin, V.

N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008—A database for evaluation of full-reference visual quality assessment metrics,” Adv. Mod. Radioelectron. 10, 30–45 (2009).

Luo, Y.

J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
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Ma, Y.

J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
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H. de Ridder and G. M. Majoor, “Numerical category scaling: an efficient method for assessing digital image coding impairments,” Proc. SPIE 1249, 65 (1990).
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Martens, J.-B.

A. M. van Dijk, J.-B. Martens, and A. B. Watson, “Quality assessment of coded images using numerical category scaling,” Proc. SPIE 2451, 90–101 (1995).
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McCulloch, M. J.

A. L. Barrowcliff, N. S. Gray, T. C. A. Freeman, and M. J. McCulloch, “Eye-movements reduce the vividness, emotional valence and electrodermal arousal associated with negative autobiographical memories,” J. Forens. Psychiatry Psychol. 15, 325–345 (2004).
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A. K. Moorthy and A. C. Bovik, “Visual quality assessment: what does the future hold?” Multimedia Tools Appl. 51, 675–696 (2011).
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A. K. Moorthy and A. C. Bovik, “Visual importance pooling for image quality assessment,” IEEE J. Sel. Top. Signal Process. 3, 193–201 (2009).
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J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
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K. Seshadrinathan, R. J. Safranek, J. Chen, T. N. Pappas, H. R. Sheikh, E. P. Simoncelli, Z. Wang, and A. C. Bovik, “Image quality assessment,” in The Essential Guide to Image Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 20.

Petry, M. C.

M. M. Bradley, M. K. Greenwald, M. C. Petry, and P. J. Lang, “Remembering pictures: pleasure and arousal in memory,” J. Exper. Psychol. Learn. Mem. Cogn. 18, 379–390 (1992).
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G. Gorn, M. T. Pham, and L. Y. Sin, “When arousal influences ad evaluation and valence does not (and vice versa),” J. Consumer Psychol. 11, 43–55 (2001).
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N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008—A database for evaluation of full-reference visual quality assessment metrics,” Adv. Mod. Radioelectron. 10, 30–45 (2009).

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A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
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K. Seshadrinathan, R. J. Safranek, J. Chen, T. N. Pappas, H. R. Sheikh, E. P. Simoncelli, Z. Wang, and A. C. Bovik, “Image quality assessment,” in The Essential Guide to Image Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 20.

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A. E. Savakis, S. P. Etz, and A. C. Loui, “Evaluation of image appeal in consumer photography,” Proc. SPIE 3959, 111–120 (2000).
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S. Tourancheau, F. Autrusseau, Z. M. P. Sazzad, and Y. Horita, “Impact of subjective dataset on the performance of image quality metrics,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp. 365–368.

Scherer, K. R.

E. S. Dan-Glauser and K. R. Scherer, “The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance,” Behav. Res. Meth. 43, 468–477 (2011).
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A. P. Hekstra, J. G. Beerends, D. Ledermann, F. E. De Caluwe, S. Kohler, R. H. Koenen, S. Rihs, M. Ehrsam, and D. Schlauss, “PVQM: a perceptual video quality measure,” Signal Process. Image Commun. 17, 781–798 (2002).
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B. N. Cuthbert, H. T. Schupp, M. M. Bradley, N. Burbaumer, and P. J. Lang, “Brain potentials in affective picture processing: covariation with autonomic arousal and affective report,” Biol. Psychol. 52, 95–111 (1999).
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K. Seshadrinathan, R. J. Safranek, J. Chen, T. N. Pappas, H. R. Sheikh, E. P. Simoncelli, Z. Wang, and A. C. Bovik, “Image quality assessment,” in The Essential Guide to Image Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 20.

K. Seshadrinathan and A. C. Bovik, “Video quality assessment,” in The Essential Guide to Video Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 14.

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H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Trans. Image Process. 15, 430–444 (2006).
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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error measurement to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
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Z. Wang, H. R. Sheikh, and A. C. Bovik, “Objective video quality assessment,” in The Handbook of Video Databases: Design and Applications, B. Furht and O. Marqure, eds. (CRC, 2003), pp 1041–1078.

K. Seshadrinathan, R. J. Safranek, J. Chen, T. N. Pappas, H. R. Sheikh, E. P. Simoncelli, Z. Wang, and A. C. Bovik, “Image quality assessment,” in The Essential Guide to Image Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 20.

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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error measurement to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
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K. Seshadrinathan, R. J. Safranek, J. Chen, T. N. Pappas, H. R. Sheikh, E. P. Simoncelli, Z. Wang, and A. C. Bovik, “Image quality assessment,” in The Essential Guide to Image Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 20.

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G. Gorn, M. T. Pham, and L. Y. Sin, “When arousal influences ad evaluation and valence does not (and vice versa),” J. Consumer Psychol. 11, 43–55 (2001).
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K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack, “Study of subjective and objective quality assessment of video,” IEEE Trans. Image Process. 19, 1427–1441 (2010).
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P. Kortum and M. Sullivan, “The effect of content desirability on subjective video quality ratings,” Hum. Factors 52, 105–118 (2010).
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D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics (Krieger, 1974).

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S. Tourancheau, F. Autrusseau, Z. M. P. Sazzad, and Y. Horita, “Impact of subjective dataset on the performance of image quality metrics,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp. 365–368.

van der Linde, I.

U. Rajashekar, I. van der Linde, A. C. Bovik, and L. K. Cormack, “Foveated analysis of image features at visual fixations,” Vis. Res. 47, 3160–3172 (2007).
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A. M. van Dijk, J.-B. Martens, and A. B. Watson, “Quality assessment of coded images using numerical category scaling,” Proc. SPIE 2451, 90–101 (1995).
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J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
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R. Datta, J. Li, and J. Z. Wang, “Algorithmic inferencing of aesthetics and emotion in natural images: an exposition,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2008), pp 105–108.

Wang, Z.

J. Ni, Huihui Jiang, Y. Jin, N. Chen, J. Wang, Z. Wang, Y. Luo, Y. Ma, and X. Hu, “Dissociable modulation of overt visual attention in valence and arousal revealed by topology of scan path,” PLoS ONE 6, e18262 (2011).
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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error measurement to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
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Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
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Z. Wang, H. R. Sheikh, and A. C. Bovik, “Objective video quality assessment,” in The Handbook of Video Databases: Design and Applications, B. Furht and O. Marqure, eds. (CRC, 2003), pp 1041–1078.

K. Seshadrinathan, R. J. Safranek, J. Chen, T. N. Pappas, H. R. Sheikh, E. P. Simoncelli, Z. Wang, and A. C. Bovik, “Image quality assessment,” in The Essential Guide to Image Processing, A. C. Bovik, ed. (Academic, 2009), Chap. 20.

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A. M. van Dijk, J.-B. Martens, and A. B. Watson, “Quality assessment of coded images using numerical category scaling,” Proc. SPIE 2451, 90–101 (1995).
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Adv. Mod. Radioelectron. (1)

N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008—A database for evaluation of full-reference visual quality assessment metrics,” Adv. Mod. Radioelectron. 10, 30–45 (2009).

Behav. Res. Meth. (1)

E. S. Dan-Glauser and K. R. Scherer, “The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance,” Behav. Res. Meth. 43, 468–477 (2011).
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Biol. Psychol. (1)

B. N. Cuthbert, H. T. Schupp, M. M. Bradley, N. Burbaumer, and P. J. Lang, “Brain potentials in affective picture processing: covariation with autonomic arousal and affective report,” Biol. Psychol. 52, 95–111 (1999).
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Commun. ACM (1)

G. K. Wallace, “The JPEG still picture compression standard,” Commun. ACM 34, 30–44 (1991).
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Hum. Brain Mapp. (1)

S. Anders, M. Lotze, M. Erb, W. Grodd, and N. Birbaumer, “Brain activity underlying emotional valence and arousal: a response-related fMRI study,” Hum. Brain Mapp. 23, 200–209 (2004).
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Hum. Factors (1)

P. Kortum and M. Sullivan, “The effect of content desirability on subjective video quality ratings,” Hum. Factors 52, 105–118 (2010).
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IEEE J. Sel. Top. Signal Process. (2)

C. Li and T. Chen, “Aesthetic visual quality assessment of paintings,” IEEE J. Sel. Top. Signal Process. 3, 236–252(2009).
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A. K. Moorthy and A. C. Bovik, “Visual importance pooling for image quality assessment,” IEEE J. Sel. Top. Signal Process. 3, 193–201 (2009).
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IEEE Signal Process. Lett. (1)

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
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IEEE Trans. Commun. (1)

A. M. Eskicioglu and P. S. Fisher, “Image quality measures and their performance,” IEEE Trans. Commun. 43, 2959–2965 (1995).
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IEEE Trans. Image Process. (5)

H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Trans. Image Process. 15, 430–444 (2006).
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N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. Image Process. 9, 636–650 (2000).
[CrossRef]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error measurement to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

D. M. Chandler and S. S. Hemami, “VSNR: a wavelet-based visual signal-to-noise ratio for natural images,” IEEE Trans. Image Process. 16, 2284–2298 (2007).
[CrossRef]

K. Seshadrinathan, R. Soundararajan, A. C. Bovik, and L. K. Cormack, “Study of subjective and objective quality assessment of video,” IEEE Trans. Image Process. 19, 1427–1441 (2010).
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J. S. Bruner and C. C. Goodman, “Value and need as organizing factors in perception,” J. Abnormal Psych. 42, 33–44 (1947).
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A. L. Bouhuys, G. M. Bloem, and T. G. G. Groothius, “Induction of depressed and elated mood by music influences the perception of facial emotional expressions in healthy subjects,” J. Affective Disord. 33, 215–226 (1995).
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Figures (6)

Fig. 1.
Fig. 1.

Valence and arousal ratings of 730 GAPED images (red dots, spiders; green dots, snakes; blue dots, positive; cyan dots, neutral; magenta dots, human concerns; gray dots, distressed animals; black circles, 100 selected images). Colored lines represent the convex hull of each image category.

Fig. 2.
Fig. 2.

Example image at each objective quality setting q5q1.

Fig. 3.
Fig. 3.

Schematic of experimental procedure.

Fig. 4.
Fig. 4.

Two-dimenional histogram showing the number of ratings (count) submitted for each subjective quality rating at each objective quality setting for all images and observers (2500 data points, interpolated for visualization only, where grid cells denote source data origin).

Fig. 5.
Fig. 5.

A, box plot showing the relationship between presentation sequence (1–5) and mean subjective quality rating (1–10); B, box plot showing the relationship between objective quality setting (q1q5) and mean subjective quality rating (1–10). Plus symbols denote outliers.

Fig. 6.
Fig. 6.

Mean subjective quality rating over time (trial number). Thin black curves represent the ratings for each presentation sequence (1–5); the thick black curve represents the mean rating.

Tables (1)

Tables Icon

Table 1. Constitution of Common Databases Used in Image Quality Assessment

Equations (1)

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[Pi+QiL>.05][|(PiQi)(Pi+Qi)|<.3].

Metrics