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Original Article

Subjective evaluation of image quality measures for white noise and Gaussian blur-distorted images

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Pages 13-21 | Accepted 11 Sep 2011, Published online: 12 Nov 2013

REFERENCES

  • Wang Z, Bovik AC, Lu LG. Why is image quality assessment so difficult, Proc. IEEE Int. Conf. on Acoustics, speech and signal processing: ICASSP ’02, Orlando, FL, USA, May 2002, IEEE, Vol. 4, pp. 3313–3316.
  • Eskicioglu AM. Quality measurement for monochrome compressed images in the past 25 years, Proc. IEEE Int. Conf. on Acoustics, speech and signal processing: ICASSP ’02, Istanbul, Turkey, June 2000, IEEE, Vol. 4, pp. 1907–1910.
  • Guo L, Meng Y. What is wrong and right with MSE, Proc. 8th IASTED Int. Conf. on Signal and image processing: SIP ’06, Honolulu, HI, USA, August 2006, IASTED, pp. 212–215.
  • Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment. Electron. Lett., 2008, 44, 800–801.
  • Barten P. Evaluation of subjective image quality with the square-root integral method. J. Opt. Soc. Am. A, 1990, 7, 2024–2031.
  • Jenkin RB, Triantaphillidou S, Richardson MA. Effective pictorial information capacity as an image quality metric. Proc. SPIE, 2007, 6494, 64940O.
  • Daly S. The visible differences predictor: an algorithm for the assessment of image fidelity, In Digital Images and Human Vision (Ed. , Watson A B), 1993, pp. 179–205 (MIT Press, Cambridge, MA).
  • Winkler S. A perceptual distortion metric for digital color images, Proc. 5th Int. Conf. on Image Processing: ICIP ’98, Chicago, IL, USA,October 1998, IEEE Computer Society, Vol. 3, pp. 399–403
  • Taylor CC. ‘Image quality assessment based on a human visual system model’, PhD thesis, Purdue University, West Lafayette, IN, USA, 1998.
  • Taylor CC, Pizlo Z, Allebach JP, Bouman CA. Perceptually relevant image fidelity assessment. Proc. SPIE, 1998, 3299, 110–118.
  • Westen SJP, Lagendijk RL, Biemond J. Perceptual image quality based on a multiple channel HVS model, Proc. IEEE Int. Conf. on Acoustic, speech and signal processing: ICASSP ’95, Detroit, MI, USA, May 1995, IEEE, pp. 2351–2354.
  • Frese T, Bouman CA, Allebac JP. Methodology for designing image similarity metrics based on human visual system models. Proc. SPIE, 1997, 3016, 472–483.
  • Miyahara M, Kotani K, Algazi VR. Objective picture quality scale (PQS) for image coding. IEEE Trans. Commun., 1998, 9, 1215–1225.
  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error measurement to structural similarity. IEEE Trans. Image Process., 2004, 13, 600–612.
  • Eskicioglu AM, Fisher PS. Image quality measures and their performance. IEEE Trans. Commun., 1995, 43, 2959–2965.
  • van Dijk M, Martens JB. Subjective quality assessment of compressed images. Signal Process., 1997, 58, 235–252.
  • Barrett HH. Objective assessment of image quality: effects of quantum noise and object variability. J. Opt. Soc. Am. A, 1990, 7, 1266–1278.
  • Sheikh HR, Sabir MF, Bovik AC. A statistical evaluation of recent full reference image quality assessment algorithm. IEEE Trans. Image Process., 2006, 15, 3440–3451.
  • Cohen E, Yitzhaky Y. No-reference assessment of blur and noise impacts on image quality. Signal Image Video Process., 2010, 4, 289–302.
  • Lee J.-C, Su Y, Tu T.-M, Chang C.-P. A novel approach to image quality assessment in iris recognition systems. Imag. Sci. J., 2010, 58, 136–145.
  • Mansoor AB, Anwar A. Subjective evaluation of image quality measures for white noise distorted images, Proc. 12th Int. Conf. on Advanced concepts for intelligent vision systems: ACIVS ’10, Sydney, Australia,December 2010 , Lecture Notes in Computer Science Volume 6474, 2010, pp 10–17.
  • Avcibas I, Sankur B. Statistical analysis of image quality measures, Proc. 10th European Signal Processing Conf.: EUSIPCO-2000, Tampere, Finland, September 2000, EURASIP, pp. 2181–2184.
  • Avcibas I, Sankur B, Sayood K. Statistical evaluation of image quality measures. J. Electron. Imag., 2002, 11, 206–223.
  • Avcibas I. ‘Image quality statistics and their use in steganalysis and compression’, PhD thesis, Bogazichi University, Istanbul, Turkey, 2001.
  • Nill NB. A visual model weighted cosine transform for image compression and quality assessment. IEEE Trans. Commun., 1985, 33, 551–557.
  • Nill NB, Bouzas BH. Objective image quality measures derived from digital image power spectra. Opt. Eng., 1992, 31, 813–825.
  • Lohmann AW, Mendelovic D, Shabtay G. Significance of phase and amplitude in the Fourier domain. J. Opt. Soc. Am. A, 1997, 14, 2901–2904.
  • Sheikh HR, Wang Z, Cormack L, Bovik AC. LIVE image quality assessment database. Available at: <http://www.live.ece.utrxas.edu/research/quality>. accessed 25 September 2012.
  • Ahumada A. J. Classification image weights and internal noise level estimation.. J. Vis., 2002, 2, article 8.
  • ‘Methodology for the subjective assessment of the quality for television pictures’, ITU-R Rec. BT. 500-11, 2002.
  • Pedersen M. 111 Full-reference image quality metrics and still not good enough? Proc. Gjøvik Color Imaging Symp. 2009: GCIS ’09, Gjøvik, Norway,June 2009, Norwegian Color Research Laboratory, No. 4, p. 4.

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