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How to assess image quality within a workflow chain: an overview

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Abstract

Image quality assessment (IQA) is a multi-dimensional research problem and an active and evolving research area. This paper aims to provide an overview of the state of the art of the IQA methods, putting in evidence their applicability and limitations in different application domains. We outline the relationship between the image workflow chain and the IQA approaches reviewing the literature on IQA methods, classifying and summarizing the available metrics. We present general guidelines for three workflow chains in which IQA policies are required. The three workflow chains refer to: high-quality image archives, biometric system and consumer collections of personal photos. Finally, we illustrate a real case study referring to a printing workflow chain, where we suggest and actually evaluate the performance of a set of specific IQA methods.

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References

  1. Alers, H., Bos, L., Heynderickx, I.: How the task of evaluating image quality influences viewing behaviour. In: The Third International Workshop on Quality of Multimedia Experience (QoMEX), Belgium (2011)

  2. ANSI: Face Recognition Format for Data Interchange. ANSI INCITS, pp. 385–2004. ANSI (2004)

  3. Archives UN: Technical guidelines for digitizing archival materials for electronic access: creation of production master files—raster images. http://www.archives.gov/preservation/technical/guidelines.html. Accessed 09 Feb 2012

  4. Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques. Data-Centric Systems and Applications. Springer Inc, New York (2006)

    Google Scholar 

  5. Batini, C., Barone, D., Cabitza, F., Ciocca, G., Marini, F., Pasi, G., Schettini, R.: Toward a unified model for information quality. In: VLDB’08 (2008)

  6. BCR’s CDP Digital Imaging Best Practices Working Group: BCR’s CDP Digital Imaging Best Practices Version 2.0. http://mwdl.org/docs/digital-imaging-bp_2.0.pdf (2008)

  7. Bhattacharya, S., Sukthankar, R., Shah, M.: A holistic approach to aesthetic enhancement of photographs. ACM Trans. Multimed. Comput. Commun. Appl. 7S, 21:1–21:21 (2011)

  8. Bovik, A., Liu, S.: Dct-domain blind measurement of blocking artifacts in dct-coded images. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1725–1728 (2001)

  9. Bovik, A.C., Wang, Z.: Modern Image Quality Assessment. Morgan & Claypool Publishers, San Rafael (2006)

  10. Brandao, T., Queluz, M.P.: No-reference image quality assessment based on dct domain statistics. Signal Process. 88(4), 822–833 (2008)

    Article  MATH  Google Scholar 

  11. Charrier, C., Lezoray, O., Lebrun, G.: Machine learning to design full-reference image quality assessment algorithm. Signal Process. Image Commun. 27, 209–219 (2012)

    Article  Google Scholar 

  12. Callet, P., Autrusseau, F.: Subjective quality assessment irc-cyn/iovc database. http://www.irccyn.ec-nantes.fr/ivcdb/ (2005)

  13. Carnec, M., Callet, P.L., Barba, D.: Objective quality assessment of color images based on a generic perceptual reduced reference. Signal Process. Image Commun. 23(4), 239–256 (2008)

    Article  Google Scholar 

  14. Chandler, D., Hemami, S.: A57 image database. http://foulard.ece.cornell.edu/dmc27/vsnr/vsnr.html (2007)

  15. Chen, C., Bloom, A.: A blind reference-free blockiness measure. In: Lecture Notes in Computer Science, vol. 6297, pp. 112–123. Springer, Berlin (2010)

  16. Chen, G.H., Yang, C.L., Xie, S.L.: Gradient-based structural similarity for image quality assessment. In: 2006 IEEE International Conference on Image Processing, pp. 2929–2932(2007)

  17. Chen, M.J., Bovik, A.C.: No-reference image blur assessment using multiscale gradient. EURASIP J. Image Video Process. 1, 1–11 (2011)

    Article  Google Scholar 

  18. Choi, M., Jung, J., Jeon, J.: No reference image quality assessment using blur and noise. Int. J. Comput. Sci. Eng. 2(3), 76–80 (2009)

  19. Ciancio, A., da Costa, A., da Silva, E., Said, A., Samadani, R., Obrador, P.: Objective no-reference image blur metric based on local phase coherence. Electron. Lett. 45(23), 1162–1163 (2009)

    Article  Google Scholar 

  20. Cohen, E., Yitzhaky, Y.: No-reference assessment of blur and noise impacts on image quality. Signal Image Video Process. 4, 289–302 (2010)

    Article  Google Scholar 

  21. Corchs, S., Gasparini, F., Marini, F., Schettini, R.: Image quality: a tool for no-reference assessment methods. In: Image Quality and System Performance VIII, IS&T/SPIE Electronic Imaging, SPIE, vol. 7867, pp. 78,760X (1–9) (2011)

  22. Corchs, S., Gasparini, F., Schettini, R.: No reference image quality classification for jpeg distorted images. Digital Signal Process. (2014a, in press)

  23. Corchs, S., Gasparini, F., Schettini, R.: Noisy images-jpeg compressed: subjective and objective image quality evaluation. In: IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, pp. 90,160V–90,160V (2014b)

  24. Corner, B.R., Narayanan, R.M., Reichenbach, S.E.: Noise estimation in remote sensing imagery using data masking. Int. J. Remote Sens. 24(4), 689–702 (2003)

    Article  Google Scholar 

  25. Crosby, P.: Quality is Free. McGraw-Hill, New York (1979)

  26. Csurka, G., Skaff, S., Marchesotti, L., Saunders, C.: Learning moods and emotions from color combinations. In: Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP ’10, pp. 298–305 (2010)

  27. Cusano, C., Ciocca, G., Schettini, R.: Image annotation using SVM. In: Proceedings of Internet Imaging, SPIE, vol. 5304, pp. 330–338 (2004)

  28. Jayaraman, D., Moorthy, A., Mittal, A., Bovik, A.: Objective quality assessment of multiply distorted images. In: Proceedings of the Asilomar Conference on Signals, Systems and Computers (2012)

  29. Daly, S.J.: Visible differences predictor: an algorithm for the assessment of image fidelity. In: Rogowitz, B.E. (ed) Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 1666, pp. 2–15 (1992)

  30. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Studying aesthetics in photographic images using a computational approach. In: Proceedings of ECCV, pp. 7–13 (2006)

  31. EC: Europeans guidelines on quality criteria for computed tomography. http://www.drs.dk/guidelines/ct/quality/. Accessed 09 Feb 2012

  32. Engeldrum, P.: A short image quality model taxonomy. J. Imaging Sci. Technol. 48(2), 160–165 (2004)

  33. Fei, X., Xiao, L., Sun, Y., Wei, Z.: Perceptual image quality assessment based on structural similarity and visual masking. Signal Process. Image Commun. 27(7), 772–783 (2012)

    Article  Google Scholar 

  34. Ferzli, R., Karam, L.J.: A no-reference objective image sharpness metric based on the notion of just noticeable blur (jnb). IEEE Trans. Image Process. 18(4), 717–728 (2009)

    Article  MathSciNet  Google Scholar 

  35. Foi, A.: Anisotropic nonparametric image restoration demobox. http://www.cs.tut.fi/lasip/2D/ (2006)

  36. Franziska, S., Frey, J.M.R.: Digital imaging for photographic collections: foundations for technical standards. Technical report, Rochester Institute of Technology, Rochester (1999)

  37. Gabarda, S., Cristóbal, G.: Blind image quality assessment through anisotropy. J. Opt. Soc. Am. A 24(12), B42–B51 (2007)

    Article  Google Scholar 

  38. Gabarda, S., Cristobal, G.: No-reference image quality assessment through the von mises distribution. J. Opt. Soc. Am. A 29(10), 2058–2066 (2012)

    Article  Google Scholar 

  39. Gasparini, F., Schettini, R.: A review of redeye detection and removal in digital images through patents. Recent Pat. Electr. Eng. 2(1), 45–53 (2009)

    Article  Google Scholar 

  40. Girod, B.: What’s wrong with mean-squared error? In: Watson, A.B. (ed.) Digital Images and Human Vision, pp. 207–220. MIT Press, Cambridge (1993)

    Google Scholar 

  41. Gonzales, R.C., Woods, R.: Digital Image Processing. Prentice Hall, Englewood Cliffs (2008)

  42. Tang, H., Joshi, N., Kapoor, A.: Learning a blind measure of perceptual image quality. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 305–312 (2011)

  43. Hasler, D., Süsstrunk, S.E.: Measuring colorfulness in natural images. In: Rogowitz, B.E., Pappas, T.N. (eds.) Human Vision and Electronic Imaging VIII. SPIE, vol. 5007, pp. 87–95 (2003)

  44. I3A: Fundamentals and review of considered test methods. CPIQ Initiative Phase 1 White Paper (2007)

  45. ICAO-NTWG: Machine readable travel documents (MRTDs): history, interoperability, and implementation. Technical report, International civil aviation organization (2007)

  46. Imatest: Digital Image Quality Testing. http://www.imatest.com (2010)

  47. Immerkaer, J.: Fast noise variance estimation. Comput. Vis. Image Underst. 64(2), 300–302 (1996)

    Article  Google Scholar 

  48. ISO: Quality management and quality assurance. vocabulary (2000). iso 84021994

  49. ISO: Image technology colour management—architecture, profile format and data structure? Part 1: based on icc.1:2004-10 (2005). iso 15076-1

  50. ISO: ISO 12233 Chart Data. http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=33715. Accessed 09 Feb 2012

  51. ISO/IEC: Biometric data interchange formats—part 5: face image data (2004). is19794-5

  52. ITU: Methodology for the subjective assessment of the quality for television pictures. Technical report, ITU-R Rec. BT. 500-13 (01.12) (2012)

  53. Janssen, T.: Computational Image Quality. SPIE Press, Bellingham (2001)

  54. Jonas, P.: Photographic Composition Simplified. Amphoto Publisher, New York (1976)

  55. Juran, J.: Juran on Planning for Quality. The Free Press, New York (1988)

    Google Scholar 

  56. Keelan, B.W.: Handbook of Image Quality: Characterization and Prediction. CRC Press, Boca Raton (2002)

  57. Kusuma, T., Zepernick, H.J.: A reduced-reference perceptual quality metric for in-service image quality assessment. In: Joint First Workshop on Mobile Future and Symposium on Trends in Communications, 2003. SympoTIC ’03, pp. 71–74 (2003)

  58. Laparra, V., Muoz, J., Malo, J.: Divisive normalization image quality metric revisited. J. Opt. Soc. Am. A 27(4), 852–864 (2010)

    Article  Google Scholar 

  59. Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1):011,006-1–011,006-21 (2010)

  60. Li, C., Bovik, A.C.: Content-partitioned structural similarity index for image quality assessment. Signal Process. Image Commun. 25(7), 517–526 (2010) (special Issue on Image and Video Quality Assessment)

  61. Li, Q., Wang, Z.: General-purpose reduced-reference image quality assessment based on perceptually and statistically motivated image representation. In: 15th IEEE International Conference on Image Processing, 2008. ICIP 2008, pp. 1192–1195 (2008)

  62. Liu, H., Heynderickx, I.: Studying the added value of visual attention in objective image quality metrics based on eye movement data. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 3097–3100 (2009)

  63. Liu, H., Redi, J., Alers, H., Zunino, R., Heynderickx, I.: Efficient neural-network based no-reference approach to an overall quality metric for jpeg and jpeg2000 compressed images. J. Electron. Imaging 20, 043,007-(1–15) (2011)

  64. Lubin, J.: A visual discrimination model for image system design and evaluation. In: Peli, E. (ed.) Visual Models for Target Detection and Recognition, pp. 207–220. World Scientific Publisher, Singapore (1995)

  65. Lundstrom, C.: Technical report: Measuring digital image quality. Technical report, Linkping UniversityLinkping University, Visual Information Technology and Applications (VITA), The Institute of Technology (2006)

  66. Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T.: A no-reference perceptual blur metric. In: IEEE 2002 International Conference on Image Processing, pp. 57–60 (2002)

  67. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    Article  MathSciNet  Google Scholar 

  68. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a completely blind image quality analyzer. IEEE Signal Process. Lett. 20, 209–212 (2013)

    Article  Google Scholar 

  69. Moorthy, A., Bovik, A.: Visual importance pooling for image quality assessment. IEEE J. Sel. Top. Signal Process. 3(2), 193–201 (2009)

    Article  Google Scholar 

  70. Moorthy, A., Bovik, A.: Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans. Image Process. 20(12), 3350–3364 (2011a)

    Article  MathSciNet  Google Scholar 

  71. Moorthy, A., Bovik, A.: Visual quality assessment algorithms: what does the future hold? Multimed. Tools Appl. 51, 675–696 (2011b)

    Article  Google Scholar 

  72. Muijs, R., Kirenko, I.: A no-reference blocking artifact measure for adaptive video processing. In: Proceedings of the 13th European Signal Processing Conference 2005 (2005)

  73. Ninassi, A., Le Meur, O., Le Callet, P., Barba, D.: Task impact on the visual attention in subjective image quality assessment. In: Proceedings of the 14th European Signal Processing Conference, Eurasip EUSIPCO (2006)

  74. Ninassi, A., Le Meur, O., Le Callet, P., Barbba, D.: Does where you gaze on an image affect your perception of quality? Applying visual attention to image quality metric. In: IEEE International Conference on Image Processing, 2007. ICIP 2007, vol. 2, pp. II-169–II-172 (2007)

  75. Nishiyama, M., Okabe, T., Sato, I., Sato, Y.: Aesthetic quality classification of photographs based on color harmony. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 33–40 (2011)

  76. Ong, E., Lin, W., Lu, Z., Yang, X., Yao, S., Pan, F., Jiang, L., Moschetti, F.: A no-reference quality metric for measuring image blur. In: Proceedings of the Seventh International Symposium on Signal Processing and Its Applications, vol. 14, pp. 469–472 (2003)

  77. Pan, F., Lin, X., Rahardja, S., Lin, W., Ong, E., Yao, S., Lu, Z., Yang, X.: A locally adaptive algorithm for measuring blocking artifacts in images and videos. Signal Process. Image Commun. 19(6), 499–506 (2004)

    Article  Google Scholar 

  78. Peli, E.: Contrast in complex images. J. Opt. Soc. Am. 7, 2032–2040 (1990)

    Article  Google Scholar 

  79. Pessoa, A., Falcao, A., e Silva, A., Nishihara, R., Lotufo, R.: Video quality assessment using objective parameters based on image segmentation. In: Proceedings of the SBT/IEEE International Telecommunications Symposium, ITS ’98, vol. 2, pp. 498–503 (1998)

  80. Pinson, M., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Trans. Broadcast. 50(3), 312–322 (2004)

    Article  Google Scholar 

  81. Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Astola, J., Carli, M., Battisti, F.: A database for evaluation of full reference visual quality assessment metrics. Adv. Mod. Radioelectron. 10, 30–45 (2009)

    Google Scholar 

  82. Rank, K., Lendl, M., Unbehauen, R.: Estimation of image noise variance. IEE Proc. Vis. Image Signal Process. 146(2), 80–84 (1999)

    Article  Google Scholar 

  83. Redi, J.A., Liu, H., Zunino, R., Heynderickx, I.: Interactions of visual attention and quality perception. In: IS&T/SPIE Electronic Imaging 2011 and Human Vision and Electronic Imaging XVI, vol. 7865 (2011)

  84. Rehman, A., Wang, Z.: Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans. Image Process. 21(8), 3378–3389 (2012)

    Article  MathSciNet  Google Scholar 

  85. Reibman, A., Bell, R., Gray, S.: Quality assessment for super-resolution image enhancement. In: 2006 IEEE International Conference on Image Processing, pp. 2017–2020 (2006)

  86. de Ridder, H., Endrikhovski, S.: Image quality is fun: reflections on fidelity, usefulness and naturalness. SID Symposium Digest of Technical Papers, vol. 33, pp. 986–989 (2002)

  87. Saad, M., Bovik, A., Charrier, C.: A dct statistics-based blind image quality index. IEEE Signal Process. Lett. 17(6), 583–586 (2010)

    Article  Google Scholar 

  88. Saad, M., Bovik, A., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the dct domain. IEEE Trans. Image Process. 21(8), 3339–3352 (2012)

    Article  MathSciNet  Google Scholar 

  89. Safranek, R., Johnston, J.: A perceptually tuned sub-band image coder with image dependent quantization and post-quantization data compression. In: 1989 International Conference on Acoustics, Speech, and Signal Processing, 1989. ICASSP-89, pp. 1945–1948 (1989)

  90. Saha, S., Vemuri, R.: An analysis on the effect of image activity on lossy coding performance. In: Proceedings of the 2000 IEEE International Symposium on Circuits and Systems, 2000. ISCAS 2000, Geneva, vol. 3, pp. 295–298 (2000)

  91. Sazzad, Z., Kawayoke, Y., Horita, Y.: Mict image quality evaluation database. http://mict.eng.u-toyama.ac.jp/mict/index2.html (2000)

  92. Schloss, K., Palmer, S.: Aesthetic response to color combinations: preference, harmony, and similarity. Atten. Percept. Psychophys. 73, 551–571 (2011)

    Article  Google Scholar 

  93. Seshadrinathan, K., Bovik, A.C.: Motion tuned spatio-temporal quality assessment of natural videos. IEEE Trans. Image Process. 19(2), 335–350 (2010)

    Article  MathSciNet  Google Scholar 

  94. Sharma, G.: Digital Color Imaging Handbook. CRC Press Inc, Boca Raton (2002)

    Book  Google Scholar 

  95. Sharma, G., Bala, R. (eds.): Digital Color Imaging Handbook, vol. 29. CRC, Boca Raton (2003)

  96. Sheikh, H., Bovik, A.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)

    Article  Google Scholar 

  97. Sheikh, H., Wang, Z., Cormack, L., Bovik, A.: Live image quality assessment database release 2 (2005)

  98. Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Annu. Rev. Neurosci. 24(1), 1193–1216 (2001)

    Article  Google Scholar 

  99. Solli, M., Lenz, R.: Color harmony for image indexing. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1885–1892 (2009)

  100. Soundararajan, R., Bovik, A.: Rred indices: reduced reference entropic differencing for image quality assessment. IEEE Trans. Image Process. 21(2), 517–526 (2012)

    Article  MathSciNet  Google Scholar 

  101. Soundararajan, R., Bovik, A.: Video quality assessment by reduced reference spatio-temporal entropic differencing. IEEE Trans. Circuits Syst. Video Technol. 23(4), 684–694 (2013)

    Article  Google Scholar 

  102. Suthaharan, S.: No-reference visually significant blocking artifact metric for natural scene images. Signal Process. 89(8), 1647–1652 (2009)

    Article  MATH  Google Scholar 

  103. TASI: Technical advisory service for images. http://www.tasi.ac.uk/advice/creating/quality.html (1979)

  104. Teo, P., Heeger, D.: Perceptual image distortion. In: Proceedings of the IEEE International Conference on Image Processing, 1994. ICIP-94, vol. 2, pp. 982–986 (1994a)

  105. Teo, P., Heeger, D.J.: Perceptual image distortion. In: Proceedings of the SPIE, pp. 982–986 (1994b)

  106. Thurstone, L.L.: A law of comparative judgement. Psychol. Rev. 34, 273–286 (1927)

    Article  Google Scholar 

  107. Tong, Y., Konik, H., Cheikh, F., Tremeau, A.: Full reference image quality assessment based on saliency map analysis. J. Imaging Sci. 54(3), 30,503-1–30,503-14 (2010)

  108. Torgerson, W.: Theory and Methods of Scaling. Wiley, Ney York (1958)

    Google Scholar 

  109. Torralba, A., Oliva, A.: Statistics of natural image categories. Netw. Comput. Neural Syst. 14(3), 391–412 (2003)

  110. Tourancheau, S., Autrusseau, F., Sazzad, Z., Horita, Y.: Impact of subjective dataset on the performance of image quality metrics. In: 15th IEEE International Conference on Image Processing, 2008. ICIP 2008, pp. 365–368 (2008)

  111. Vlachos, T.: Detection of blocking artifacts in compressed video. Electron. Lett. 36(13), 1106–1108 (2000)

    Article  Google Scholar 

  112. VQEG: Vqeg final report of fr-tv phase ii validation test. Technical report, Video Quality Experts Group (VQEG) (2003)

  113. Wang, Z., Bovik, A.: Mean squared error: love it or leave it? A new look at signal fidelity measures. IEEE Signal Process. Mag. 26(1), 98–117 (2009)

    Article  Google Scholar 

  114. Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Trans. Image Process. 20(5), 1185–1198 (2011)

    Article  MathSciNet  Google Scholar 

  115. Wang, Z., Simoncelli, E.P.: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Proceedings of the SPIE Human Vision and Electronic Imaging, vol. 5666, pp. 149–159 (2005)

  116. Wang, Z., Bovik, A., Evans, B.: Blind measurement of blocking artifacts in images. In: Proceedings of the IEEE International Conference on Image Processing, pp. 981–984 (2000)

  117. Wang, Z., Sheikh, H., Bovik, A.: No-reference perceptual quality assessment of jpeg compressed images. In: Proceedings of the 2002 International Conference on Image Processing, vol. 1, pp. I-477–I-480 (2002)

  118. Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2004, vol. 2, pp. 1398–1402 (2003)

  119. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004a)

    Article  Google Scholar 

  120. Wang, Z., Simoncelli, E.P., Hughes, H.: Local phase coherence and the perception of blur. In: Advances in Neural Information Processing Systems, NIPS03, pp. 786–792. MIT Press, Cambridge (2004b)

  121. Watson, A.B.: DCT quantization matrices visually optimized for individual images. In: Allebach, J.P., Rogowitz, B.E. (eds.) Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 1913, pp. 202–216 (1993)

  122. Watson, A.B., Borthwick, R., Taylor, M.: Image quality and entropy masking. In: SPIE Human Vision and Electronic Imaging Conference, vol. 3016, pp. 2–12 (1997)

  123. Wayne, S.: Quality control circle and company wide quality control. Qual. Prog. 16(10), 14–17 (1983)

  124. Wee, C.Y., Paramesran, R., Mukundan, R., Jiang, X.: Image quality assessment by discrete orthogonal moments. Pattern Recognit. 43(12), 4055–4068 (2010)

    Article  MATH  Google Scholar 

  125. Winkler, S., Süsstrunk, S.: Visibility of noise in natural images. In: Proceedings of the IS&T/SPIE Electronic Imaging 2004: Human Vision and Electronic Imaging IX, vol. 5292, pp. 121–129 (2004)

  126. Wu, H., Yuen, M.: A generalized block-edge impairment metric for video coding. IEEE Signal Process. Lett. 4, 317–320 (1997)

    Article  Google Scholar 

  127. X-Rite: X-Rite ColorChecker Classic. http://xritephoto.com/ph_product_overview.aspx?ID=1192. Accessed 09 Feb 2012

  128. Ye, P., Doermann, D.: No-reference image quality assessment using visual codebooks. IEEE Trans. Image Process. 21(7), 3129–3138 (2012)

    Article  MathSciNet  Google Scholar 

  129. Yeganeh, H., Wang, Z.: Objective quality assessment of tone-mapped images. IEEE Trans. Image Process. 22(2), 657–667 (2013)

    Article  MathSciNet  Google Scholar 

  130. Yeganeh, H., Rostami, M., Wang, Z.: Objective quality assessment for image super-resolution: a natural scene statistics approach. In: 2012 19th IEEE International Conference on Image Processing (ICIP), pp. 1481–1484 (2012)

  131. Yen, R., Zektser, G.: A new approach for measuring facial image quality. Def. Stand. Progr. J. 13–25 (2008)

  132. Yendrikhovskij, S.: Image quality: between science and fiction. In: PICS, pp. 173–178 (1999)

  133. Zhang, D.Q., Chang, S.F.: Detecting image near-duplicate by stochastic attributed relational graph matching with learning. In: Proceedings of the 12th Annual ACM International Conference on Multimedia, MULTIMEDIA ’04, pp. 877–884 (2004)

  134. Zhang, L., Zhang, D., Mou, X., Zhang, D.: Fsim: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  Google Scholar 

  135. Zhang, X., Wandell, B.A.: A spatial extension of cielab for digital color-image reproduction. J. Soc. Inf. Disp. 5(1), 61–63 (1997)

    Article  Google Scholar 

  136. Zhu, X., Milanfar, P.: Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans. Image Process. 19(12), 3116–3132 (2010)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

The authors would like to thank Fabrizio Marini for the insightful discussions on image quality and for his work in developing the no reference image quality tool. This work was partially supported by Océ-Canon.

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  1. DISCo, University of Milano Bicocca, Milan, Italy

    Gianluigi Ciocca, Silvia Corchs, Francesca Gasparini & Raimondo Schettini

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Ciocca, G., Corchs, S., Gasparini, F. et al. How to assess image quality within a workflow chain: an overview. Int J Digit Libr 15, 1–25 (2014). https://doi.org/10.1007/s00799-014-0124-0

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