Reduced-reference metric design for objective perceptual quality assessment in wireless imaging

Signal Processing: Image Communication

Volumn 24, Issue 7, 2009, Pages 525-547

  Engelke, Ulrich ^a   Kusuma, Maulana ^b   Zepernick, Hans Jürgen ^a   Caldera, Manora ^c  

^a BLEKINGE INSTITUTE OF TECHNOLOGY   (Sweden)

^b GUNADARMA UNIVERSITY   (Indonesia)

^c Gibson Quai AAS Consulting   (Australia)

Author keywords

Normalized hybrid image quality metric; Objective perceptual image quality; Perceptual relevance weighted Lp norm; Reduced reference; Wireless imaging

Indexed keywords

NORMALIZED HYBRID IMAGE QUALITY METRIC; OBJECTIVE PERCEPTUAL IMAGE QUALITY; PERCEPTUAL RELEVANCE WEIGHTED LP-NORM; REDUCED-REFERENCE; WIRELESS IMAGING;

BENCHMARKING; FEATURE EXTRACTION; HUMAN COMPUTER INTERACTION; METRIC SYSTEM; QUALITY OF SERVICE; WIRELESS TELECOMMUNICATION SYSTEMS;

IMAGE QUALITY;

EID: 67849103559     PISSN: 09235965     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.image.2009.06.005     Document Type: Article

References (45)

1. A.C. Brooks, T.N. Pappas, Structural similarity quality metrics in a coding context: exploring the space of realistic distortions, in: IS&T/SPIE Human Vision and Electronic Imaging XI, vol. 6057, January 2006, pp. 60570U-1-12.
2. Carnec M., Le Callet P., and Barba D. Objective quality assessment of color images based on a generic perceptual reduced reference. Image Commun. 23 4 (April 2008) 239-256
3. D.M. Chandler, K.H. Lim, S.S. Hemami, Effects of spatial correlations and global precedence on the visual fidelity of distorted images, in: IS&T/SPIE Human Vision and Electronic Imaging XI, vol. 6057, January 2006, pp. 131-145.
4. Chandler D.M., and Hemami S.S. VSNR: a wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans. Image Process. 16 9 (September 2007) 2284-2298
5. K. Chono, Y.-C. Lin, D. Varodayan, Y. Miyamoto, B. Girod, Reduced-reference image quality assessment using distributed source coding, in: IEEE International Conference on Multimedia and Expo, June 2008, pp. 609-612
6. S. Daly, Visible differences predictor: an algorithm for the assessment of image fidelity, in: IS&T/SPIE Human Vision, Visual Processing, and Digital Display III, vol. 1666, August 1992, pp. 2-15.
7. U. Engelke, H.-J. Zepernick, Quality evaluation in wireless imaging using feature-based objective metrics, in: IEEE International Symposium on Wireless Pervasive Computing, San Juan, Puerto Rico, February 2007, pp. 367-372.
8. Eskicioglu A.M., and Fisher P.S. Image quality measures and their performance. IEEE Trans. Commun. 43 12 (December 1995) 2959-2965
9. M.C.Q. Farias, M.S. Moore, J.M. Foley, S.K. Mitra, Perceptual contributions of blocky, blurry, and fuzzy impairments to overall annoyance, in: Proceedings of the IS&T/SPIE Human Vision and Electronic Imaging IX, vol. 5292, 2004, pp. 109-120.
10. M.C.Q. Farias, J.M. Foley, S.K. Mitra, Perceptual analysis of video impairments that combine blocky, blurry, noisy, and ringing synthetic artifacts, in: IS&T/SPIE Human Vision and Electronic Imaging X, vol. 5666, 2005, pp. 107-118.
11. R. Ferzli, L.J. Karam, J. Caviedes, A robust image sharpness metric based on kurtosis measurement of wavelet coefficients, in: International Workshop on Video Processing and Quality Metrics for Consumer Electronics, January 2005.
12. Girod B. What's wrong with mean-squared error. In: Watson A.B. (Ed). Digital Images and Human Vision (1993), MIT Press, Cambridge, MA 207-220
13. ITU-R Recommendation BT.814, Specifications and alignment procedures for setting of brightness and contrast of displays, Geneva, Switzerland, 1994.
14. ITU-R Recommendation BT.815, Specification of a signal for measurement of the contrast ratio of displays, Geneva, Switzerland, 1994.
15. ITU-R Recommendation BT.1129-2, Subjective assessment of standard definition digital television (SDTV) systems, Geneva, Switzerland, 1998.
16. ITU-R Recommendation BT.500-10, Methodology for the subjective assessment of the quality of television pictures, Geneva, Switzerland, 2002.
17. Kusuma M., and Zepernick H.-J. Objective hybrid image quality metric for in-service quality assessment. Signal Processing for Telecommunications and Multimedia (2005), Springer, Berlin 44-55
18. T.M. Kusuma, A perceptual-based objective quality metric for wireless imaging, Ph.D. Thesis, Curtin University of Technology, Perth, Australia, 2005.
19. Lubin J. A visual discrimination model for imaging system design and evaluation. In: Peli E. (Ed). Vision Models for Target Detection and Recognition (1995), World Scientific, Singapore 245-283
20. Lukas F.X.J., and Budrikis Z.L. Picture quality prediction based on a visual model. IEEE Trans. Commun. 30 6 (July 1982) 1679-1692
21. Martens J.B. Multidimensional modeling of image quality. Proc. IEEE 90 1 (January 2002) 133-153
22. P. Marziliano, F. Dufaux, S. Winkler, T. Ebrahimi, A no-reference perceptual blur metric, in: IEEE International Conference on Image Processing, September 2002, pp. 57-60.
23. Meesters L., and Martens J.B. A single ended blockiness measure for JPEG coded images. Signal Process. 82 (March 2002) 369-387
24. Molisch A.F. Wireless Communications (2005), Wiley, IEEE Press, New York
25. Ohm J.-R. Multimedia Communication Technology: Representation, Transmission and Identification of Multimedia Signals (2004), Springer, Berlin
26. F. Pereira, Sensations, perceptions and emotions towards quality of experience evaluation for consumer electronics video adaptations, in: International Workshop on Video Processing and Quality Metrics for Consumer Electronics, January 2005.
27. H. de Ridder, Minkowski-metrics as a combination rule for digital-image-coding impairments, in: IS&T/SPIE Human Vision, Visual Processing, and Digital Display III, vol. 1666, January 1992, pp. 16-26.
28. de Ridder H. Percentage scaling: a new method for evaluating multiple impaired images. Proc. SPIE 3959 (2000) 68-77
29. Rix A.W., Bourret A., and Hollier M.P. Models of human perception. J. BT Technol. 17 1 (January 1999) 24-34
30. Saha S., and Vemuri R. An analysis on the effect of image features on lossy coding performance. IEEE Signal Process. Lett. 7 5 (May 2000) 104-107
31. Sheikh H.R., and Bovik A.C. Image information and visual quality. IEEE Trans. Image Process. 15 2 (February 2006) 430-444
32. D. Soldani, M. Li, R. Cuny (Ed.), QoS and QoE Management in UMTS Cellular Systems, Wiley, New York, 2006.
33. C.J. van den Branden Lambrecht, O. Verscheure, Perceptual quality measure using a spatio-temporal model of the human visual system, in: SPIE Digital Video Compression: Algorithms and Technologies, vol. 2668, January 1996, pp. 450-460.
34. Video Quality Experts Group, Final report from the Video Quality Experts Group on the validation of objective models of video quality assessment phase II, August 2003, [Online]. Available: 〈http://www.vqeg.org.〉
35. Vlachos T. Detection of blocking artifacts in compressed video. IEE Electron. Lett. 36 13 (June 2000) 1106-1108
36. Wandell B.A. Foundations of Vision (1995), Sinauer Associates
37. Z. Wang, A.C. Bovik, L. Lu, Why is image quality assessment so difficult? in: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, May 2000, pp. 3313-3316.
38. Z. Wang, H.R. Sheikh, A.C. Bovik, No-reference perceptual quality assessment of JPEG compressed images, in: IEEE International Conference on Image Processing, September 2002, pp. 477-480.
39. Wang Z., Bovik A.C., Sheikh H.R., and Simoncelli E.P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 4 (April 2004) 600-612
40. Z. Wang, E.P. Simoncelli, Reduced-reference image quality assessment using a wavelet-domain natural image statistic model, in: IS&T/SPIE Human Vision and Electronic Imaging X, vol. 5666, March 2005, pp. 149-159.
41. Wang Z., Wu G., Sheikh H.R., Simoncelli E.P., Yang E.H., and Bovik A.C. Quality aware images. IEEE Trans. Image Process. 15 6 (June 2006) 1680-1689
42. Watson A.B., Hu J., and McGowan J.F. Digital video quality metric based on human vision. SPIE J. Electron. Imaging 10 1 (January 2001) 20-29
43. Winkler S. Digital Video Quality (2005), Wiley, New York
44. H.R. Wu, K.R. Rao (Eds.), Digital Video Image Quality and Perceptual Coding, CRC Press, Boca Raton, FL, 2006.
45. T. Yamada, Y. Miyamoto, M. Serizawa, H. Harasaki, Reduced-reference based video quality metrics using representative luminance values, in: International Workshop on Video Processing and Quality Metrics for Consumer Electronics, January 2007.