A Universal Color Image Quality Metric

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5108, Issue , 2003, Pages 13-23

  Toet, Alexander ^a   Lucassen, Marcel P ^a  

^a TNO   (Netherlands)

Author keywords

Color fidelity; Image quality

Indexed keywords

COLOR IMAGE PROCESSING; COMPUTATIONAL COMPLEXITY; IMAGE COMPRESSION; MATHEMATICAL MODELS; SIGNAL TO NOISE RATIO; VECTORS;

COLOR FIDELITY;

IMAGE QUALITY;

EID: 0344944732     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.484836     Document Type: Conference Paper

References (51)

1. Gaddipatti, A., Machiraju, R. and Yagel, R., Steering image generation with wavelet based perceptual metric, EUROGRAPHICS '97, 16(3), pp. C-241-C-251, 1997.
2. Mannos, J.L. and Sakrison, D.J., The effects of a visual fidelity criterion on the encoding of images, IEEE Transactions on Information Theory, 20(4), pp. 525-536, 1974.
3. Saghri, J.A., Image quality measure based on a human visual system model, Optical Engineering, 28(7), pp. 813-818, 1989.
4. Karunasekera, S.A. and Kingsbury, N.G., A distortion measure for blocking artifacts in images based on human visual sensitivity, IEEE Transactions on Image Processing, 4(6), pp. 713-724, 1995.
5. Eskicioglu, A.M. and Fisher, P.S., Image quality measures and their performance, IEEE Transactions on Communications, 43(12), pp. 2959-2965, 1995.
6. Jayant, N., Johnston, J. and Safranek, R., Signal compression based on models of human perception, Proceedings of the IEEE, 81(70), pp. 1385-1422, 1993.
7. Lubin,J. (1995). A visual discrimination model for imaging system design and evaluation. In: E.Peli (Ed.), Vision models for target detection and recognition, (pp. 245-283). Singapore: World Scientific.
8. Daly, S.J., The visible differences predictor: an algorithm for the assessment of image fidelity, In: A.B. Watson (Ed.), Digital Imaging and Human Vision, pp. 179-206, MIT Press, Cambridge, MA, 1993.
9. Watson, A.B., Hu, J., McGowan, J.F. and Mulligan, J.B., Design and performance of a digital video quality metric, In: Human Vision, Visual Processing, and Digital Display IX, SPIE-3644, pp. 168-174, The Society of Photo-Optical Instrumentation Engineers, Bellingham, WA, 1999.
10. Watson, A.B., Hu, J. and McGowan, J.F., Digital video quality metric based on human vision, Journal of Electronic Imaging, 10(1), pp. 20-29, 2001.
11. Watson, A.B., Toward a perceptual video quality metric, In: Human Vision and Electronic Imaging III, SPIE-3299, pp. 139-147, The Society of Photo-Optical Instrumentation Engineers, Bellingham,WA, 1998.
12. Teo, P.C. and Heeger, D.J., Perceptual image distortion, In: Human Vision, Visual Processing, and Digital Display V, SPIE-2179, pp. 127-141, The International Society for Optical Engineering, Bellingham, WA, 1994.
13. van den Branden Lambrecht, C.J., Color moving pictures quality metric, In: Proceedings of the IEEE International Conference on Image Processing, 1, pp. 885-888, 1996.
14. Ahumada, A.J., Computational image quality metrics: a review, In: J. Morreale (Ed.), Society for Information Display International Symposium Digest of Technical Papers, 24, pp. 305-308, Society for Information Display, Playa del Rey, California, USA, 1993.
15. Avadhanam, N. and Algazi, V.R., Evaluation of a human-vision-system-based image fidelity metric for image compression, In: A.G. Tescher (Ed.), Applications of Digital Image Processing XXII, SPIE-3808, pp. 569-579, The International Society for Optical Engineering, Bellingham, WA, 1999.
16. Avcibas, I., Sankur, B. and Sayood, K., Statistical evaluation of image quality measure, Journal of Electronic Imaging, 11(2), pp. 206-223, 2002.
17. Bolin, M R., Meyer, G.W. and Aase, S.O., A visual difference metric for realistic image synthesis, In: B.E. Rogowitz & T.N. Pappas (Ed.), Human Vision and Electronic Imaging IV, SPIE-3644, pp. 106-120, The International Society for Optical Engineering, Bellingham, WA, 1999.
18. Daly, S.J., Visible differences predictor: an algorithm for the assessment of image fidelity, In: B.E. Rogowitz (Ed.), Human Vision, Visual Processing, and Digital Display III, SPIE-1666, pp. 2-15, The International Society for Optical Engineerin, Bellingham, WA, USA, 2000.
19. Farrell, J.E., Zhang, X. and van den Branden Lambrecht, C.J., Image quality metrics based on single and multichannel models of visual processing, In: Proceedings of COMPCON, pp. 1997.
20. Janssen, R., Computational image quality, The International Society for Optical Engineering, Bellingham, WA, 2001.
21. Janssen, T.J.W.M. and Blommaert, F.J.J., A computational approach to image quality, Displays, 21, pp. 129-142, 2000.
22. Janssen, T.J.W.M. and Blommaert, F.J.J., Information processing approach to image quality, In: B.E. Rogowitz & T.N. Pappas (Ed.), Human Vision and Electronic Imaging V, SPIE-3959, pp. 78-87, The International Society for Optical Engineering, Bellingham, WA., USA, 2000.
23. Jung, M., Léger, D. and Gazalat, M., Univariant assessment of the quality of images, Journal of Electronic Imaging, 11(3), pp. 354-364, 2002.
24. Lai, Y.-K. and Kuo, C.-C.J., A Haar wavelet approach to compressed image quality measurement, Journal of Visual Communication and Image Representation, 11(1), pp. 17-40, 2000.
25. Lai, Y.-K., Guo, J. and Kuo, C.-C.J., Perceptual fidelity measure of digital color images, In: B.E. Rogowitz & T.N. Pappas (Ed.), Human Vision and Electronic Imaging III, SPIE-3299, pp. 221-231, The International Society for Optical Engineering, Bellingham, WA., USA, 2002.
26. Lian, M.-S., Image evaluation using a color visual difference predictor (CVDP), In: B.E. Rogowitz & T.N. Pappas (Ed.), Human Vision and Electronic Imaging VI, SPIE-4299, pp. 175-186, The International Society for Optical Engineering, Bellingham,WA, 2001.
27. Malo, J., Pons, A.M. and Artigas, J.M., Subjective image fidelity metric based on bit allocation of the human visual system in the DCT domain, Image and Vision Computing, 15(7), pp. 535-548, 1997.
28. van den Branden Lambrecht, C.J., A working spatio-temporal model of the human visual system for image restoration and quality assessment applications, In: Proceedings of ICASSP 96, pp. 2000.
29. Nill, N.B., A visual model weighted cosine transform for image compression and quality assessment, IEEE Transactions on Communications, 33(6), pp. 551-557, 1985.
30. Wu, H.R., van den Branden Lambrecht, C.J., Yuen, M. and Qin, B., Quantitative quality and impairment metrics for digital coded images and image sequences, In: Proceedings of the Australian Telecommunication Networks & Applications Conference, pp. 1996.
31. Zhang, X. and Wandell, B.A., Colour image fidelity metrics evaluated using image distortion maps, Signal Processing, 70(3), pp. 201-214, 1998.
32. Fuhrmann, D.R., Baro, J.A. and Cox, J.R., Experimental evaluation of psychophysical distortion metrics for JPEG-encoded images, Journal of Electronic Imaging, 4(4), pp. 397-406, 1995.
33. Fdez-Vidal, X.R., Toet, A., Garcia, J.A. and Fdez-Valdivia, J., Computing visual target distinctness through selective filtering, statistical features, and visual patterns, Optical Engineering, 39(1), pp. 267-281, 2000.
34. Garcia, J.A., Fdez-Valdivia, J., Fdez-Vidal, X.R. and Rodriguez-Sánchez, R., Computational models for predicting visual target distinctness, SPIE Optical Engineering Press, Bellingham, Washington, USA, 2001.
35. Garcia, J.A., Fdez-Valdivia, J., Fdez-Vidal, X.R. and Rodriguez-Sánchez, R., Information theoretic measure for visual target distinctness, IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI, 23(4), pp. 362-383, 2001.
36. Toet, A. Computing visual target distinctness, (Report TNO-TM 1997 A-039), TNO Human Factors, Soesterberg, The Netherlands, (1997).
37. Griswold, N.C., Perceptual coding in the cosine transform domain, Optical Engineering, 19(3), pp. 306-311, 1980.
38. Jain, A.K., Image data compression: a review, Proceedings of the IEEE, 69(3), pp. 349-389, 1981.
39. Limb, J.O., Distortion criteria of the human observer, IEEE Transactions on Systems, Man and Cybernetics SMC, 9(12), pp. 778-793, 1979.
40. Lukas, F.X.J. and Budrikis, Z.L., Picture quality prediction based on a visual model, IEEE Transactions on Communications, 30(7), pp. 1679-1692, 1982.
41. Marmolin, H., Subjective MSE measures, IEEE Transactions on Systems, Man and Cybernetics SMC, 16, pp. 486-489, 1986.
42. Winkler, S., Perceptual distortion metric for digital color video, In: B.E. Rogowitz & T.N. Pappas (Ed.), Human Vision and Electronic Imaging IV, SPIE-3644, pp. 175-184, The International Society for Optical Engineering, Bellingham, WA, 1999.
43. Le Callet, P. and Barba, D., Perceptual color image quality metric using adequate error, In: B.E. Rogowitz & T.N. Pappas (Ed.), Human Vision and Electronic Imaging VII, SPIE-4662, pp. 173-180, The International Society for Optical Engineering, Bellingham, WA., USA, 2002.
44. Zhang, X., Silverstein, D.A., Farrell, J.E. and Wandell, B.A., Color Image Quality Metric S-CIELAB and Its Application on Halftone Texture Visibility, In: pp. 2000.
45. Wang, Z. and Bovik, A.C., A universal image quality index, IEEE Signal Processing Letters, 9(3), pp. 81-84, 2002.
46. Ruderman, D.L., Cronin, T.W. and Chiao, C.-C., Statistics of cone responses to natural images: implications for visual coding, Journal of the Optical Society of America A, 15(8), pp. 2036-2045, 1998.
47. Buchsbaum, G. and Gottschalk, A., Trichromacy, opponent colours coding and optimum colour information transmission in the retina, Proceedings of the Royal Society of London B, 220(1218), pp. 89-113, 1983.
48. Fairchild, M.D., Color appearance models, Addison Wesley Longman, Inc., Reading, MA, 1998.
49. CIE. Industrial colour difference evaluation, (Report CIE 116), International Commission on Illumination CIE, Vienna, Austria, (1995).
50. nd edition, McGraw-Hill, Boston. MA, 1988.
51. Trémeau, A. and Charrier, C., Influence of chromatic changes on the perception of color image quality. Color Research and Application, 25(3), pp. 200-213, 2000.