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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Fry, E.; Triantaphillidou, S.; Jarvis, J.; Gupta, G. (2015)
Publisher: SPIE
Languages: English
Types: Unknown
Subjects: UOWSAT
What is the best luminance contrast weighting-function for image quality optimization? Traditionally measured contrast sensitivity functions (CSFs), have been often used as weighting-functions in image quality and difference metrics. Such weightings have been shown to result in increased sharpness and perceived quality of test images. We suggest contextual CSFs (cCSFs) and contextual discrimination functions (cVPFs) should provide bases for further improvement, since these are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context of complex masking information. Image quality assessment is understood to require detection and discrimination of masked signals, making contextual sensitivity and discrimination functions directly relevant. In this investigation, test images are weighted with a traditional CSF, cCSF, cVPF and a constant function. Controlled mutations of these functions are also applied as weighting-functions, seeking the optimal spatial frequency band weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice psychophysical tests. We show that maximal quality for our test images, results from cCSFs and cVPFs, mutated to boost contrast in the higher visible frequencies.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Triantaphillidou S, Jarvis J, Gupta G. Spatial contrast sensitivity and discrimination in pictorial images. In Proc. SPIE 9016, Image Quality and System Performance XI, 901604; 2014. p. 10.1117/12.2040007.
    • 2. Haun A, Peli E. Perceived contrast in complex images. Journal of Vision. 2013; 13(13): p. 1-21.
    • 3. Keelan B,W. Handbook of Image Quality: Characterization and Prediction: Marcel Dekker; 2002.
    • 4. Bouzit S, MacDonald L,W. Does sharpness affect the reproduction of colour images. In Proc. SPIE 4421, 9th Congress of the International Colour Association; 2002. p. 902-905.
    • 5. Barten P,G,J. Contrast sensitivity of the human eye and its effects on image quality Washington: SPIE - The Society of Photo-Optical Instrumentation Engineers; 1999.
    • 6. Haun AM, Peli E. Is image quality a function of contrast perception? In Proc. of SPIE 8651; 2013.
    • 7. Johnson G,M, Fairchild M,D. A top down description of S-CIELAB and CIEDE2000. Col. Res. & App. 28(6). 2003;: p. 425-435.
    • 8. Granger E,M, Cupery K,N. An optical merit function (SQF) which correlates with subjective image judgements. Photograph. Sci. Engng. 1972; 16(3): p. 221-30.
    • 9. Snyder H,L. Image quality and observer performance. In Biberman LM, editor. Perception of displayed information. New York and London: Plenum Press; 1973. p. 87-118.
    • 10. Topfer K, Jacobson R,E. The relationship between objective and subjective image quality. J. Inf. Rec. Mats. 1993; 21: p. 5-27.
    • 11. Jenkin R, Triantaphillidou S, Richardson M. Effective pictorial information capacity as an image quality metric. Proc. IS&T/SPIE 6494 Electronic Imaging: Image Quality and System Performance IV, 64940O. 2007.
    • 12. Triantaphillidou S, Jarvis J, Gupta G. Contrast sensitivity and discrimination of complex scenes. Proc. SPIE 8653, Im. Qual. & Sys. Perf. X, 86530C. 2013.
    • 13. Triantaphillidou S, Jarvis J, Gupta G, Rana H. Defining human contrast sensitivity and discrimination from complex imagery. In Proc. SPIE 8901, Optics and Photonics for Counterterrorism, Crime Fighting and Defence IX; and Optical Materials and Biomaterials in Security and Defence Systems Technology X; 2013.
    • 14. Engeldrum P,G. Psychometric scaling: A toolkit for imaging systems development,: Imcotek Press, Winchester MA; 2000.
    • 15. Jacobsen R, Triantaphillidou S. Metric approaches to image quality. In Macdonald L, Ronnier Luo M, editors. Color image science: Exploiting digital media.: Wiley, UK; 2002. p. 371-392.
    • 16. Battiato S, Castorina A, Guarnera M, Vivirito P. A global enhancement pipeline for low-cost imaging devices. IEEE Transactions on Consumer Electronics. 2003; 49(3): p. 670-675.
    • 17. Haun AM, Peli E. Measuring the perceived contrast of natural images. In SID Symposium Digest of Techical Papers; 2011. p. 302-304.
    • 18. Bouzit S, Macdonald L,W. Sharpness enhancement through spatial frequency decomposition. In PICS 2001: Image Processing, Image Quality, Image Capture Systems Conference; 2001; Montreal. p. 337-381.
    • 19. Zakia R, Stroebel L. The Focal Encyclopedia of Photography: Focal Press; 1993.
    • 20. Yendrikhovskij S. Image quality and color categorization. In Macdonald L, Ronnier Luo M, editors. Color Image Science: Exploiting Digital Media.: Wiley, UK.; 2002. p. 393-419.
    • 21. Fedorovskaya E, de Ridder H, Blommaert F. Chroma variations and perceived quality of color images of natural scenes. Color Research and Application. 1998; 22(2): p. 96-110.
    • 22. Bouzit S, MacDonald L,W. Assessing the enhancement of image sharpness. In Cui LC, Miyake Y, editors. Proc. SPIE 6059, Image Quality and System Performance III; 2006. p. 605904-1.
    • 23. Peli E. Contrast in complex images. JOSA A, Vol. 7, Issue 10. 1990;: p. 2032-2040.
    • 24. Parraga CA, Brelstaff G, Troscianko T, Moorehead IR. Color and luminance information in natural scenes. J Opt Soc
    • 25. Peli E. Contrast sensitivity function and image discrimination. J. Opt. Soc. A. A. 2001; 18: p. 283-293.
    • 26. Kim SH, Allebach JP. Optimal unsharp mask for image sharpening and noise removal. Journal of Electronic Imaging. 2005 Apr; 14(2).
    • 27. MacDonald L,W, Bouzit S. Internet-based assessment of image sharpness enhancement. In Farnland S, Gaykema F, editors. SPIE 6808, Image Quality and System Performance V; 2008. p. 680812-1.
    • 28. ISO (International Standards Organisation). Photography -- Electronic Still Picture Cameras -- Resolution Measurements. ; 2000.
    • 29. ISO (International Standards Organisation). ISO 14524 Photography -- Electronic Still-Picture Cameras -- Methods for Measuring Opto-Electronic Conversion Functions (OECFs). ; 1999.
    • 30. Berns R. Methods for characterizing CRT displays. Displays. 1996; 16(4): p. 173-182.
    • 31. Jarvis J, Prescott N, Wathes C. A mechanistic inter-species comparison of spatial contrast sensitivity. Vision Res. 2008; 48: p. 2284-2292.
    • 32. Farinella GM, Battiato S, Gallo G, Cipolla R. Natural versus artificial scene classification by ordering discrete Fourier spectra. In Proc. 12th International Workshop on Structural and Syntactic Pattern Recognition (SSPR); 2008. p. 137-146.
    • 33. Jarvis J, Prescott N, Wathes C. A mechanistic inter-species comparison of flicker sensitivity. Vision Res. 2003; 43: p. 1723-1734.
    • 34. Johnson G,M, Fairchild M,D. Sharpness Rules. Proc 8th. IS&T/SID Color Imaging Conference. 2000;: p. 24-30.
    • 35. Winston R. Canon. [Online].; 2013 [cited 2014 10 18]. Available from: http://learn.usa.canon.com/resources/articles/2013/reading_MTF_charts.shtml.
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