JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Elaborate Image Quality Assessment with a Novel Luminance Adaptation Effect Model
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of Broadcast Engineering
  • Volume 20, Issue 6,  2015, pp.818-826
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2015.20.6.818
 Title & Authors
Elaborate Image Quality Assessment with a Novel Luminance Adaptation Effect Model
Bae, Sung-Ho; Kim, Munchurl;
  PDF(new window)
 Abstract
Recently, objective image quality assessment (IQA) methods that elaborately reflect the visual quality perception characteristics of human visual system (HVS) have actively been studied. Among those characteristics of HVS, luminance adaptation (LA) effect, indicating that HVS has different sensitivities depending on background luminance values to distortions, has widely been reflected into many existing IQA methods via Weber's law model. In this paper, we firstly reveal that the LA effect based on Weber's law model has inaccurately been reflected into the conventional IQA methods. To solve this problem, we firstly derive a new LA effect-based Local weight Function (LALF) that can elaborately reflect LA effect into IQA methods. We validate the effectiveness of our proposed LALF by applying LALF into SSIM (Structural SIMilarity) and PSNR methods. Experimental results show that the SSIM based on LALF yields remarkable performance improvement of 5% points compared to the original SSIM in terms of Spear rank order correlation coefficient between estimated visual quality values and measured subjective visual quality scores. Moreover, the PSNR (Peak to Signal Noise Ratio) based on LALF yields performance improvement of 2.5% points compared to the original PSNR.
 Keywords
Human visual system (HVS);luminance adaptation (LA);image quality assessment (IQA);power law;Weber's law;
 Language
Korean
 Cited by
 References
1.
Z. Wang and A. C. Bovik, “Mean squared error: Love it or leave it? A new look at signal fidelity measures,” IEEE Signal Process. Mag., vol. 26, no. 1, pp. 98-117, Jan. 2009. crossref(new window)

2.
S.-H. Bae, J. Kim, M. Kim, S. H. Cho, and J. S. Choi, “Assessments of subjective video quality on HEVC-encoded 4K-UHD video for beyond-HDTV broadcasting services,” IEEE Trans. on Broadcast., vol. 59, no. 2, pp. 209-222, Jun. 2013. crossref(new window)

3.
J.-S. Choi, S.-H. Bae and M. Kim, “Single image super-resolution based on self-examples using context-dependent subpatches,” IEEE Int. Conf. on Image Proc, accepted for publication, Sept. 27-30, 2015.

4.
J.-S. Choi, S.-H. Bae and M. Kim, "A no-reference perceptual blurriness metric based fast super-resolution of still pictures using sparse representation," Proc. SPIE, vol. 9401, pp. 94010N.1-94010N.7, Mar. 2015.

5.
J. Kim, S.-H. Bae, and M. Kim, “An HEVC-compliant perceptual video coding scheme based on JND models for variable block-sized transform kernels,” IEEE Trans. Circuits Syst. Video Technol., in press, Jan. 2014.

6.
L. Zhang, L. Zhang, X. Mou, and D. Zhang, “A comprehensive evaluation of full reference image quality assessment algorithms,” Proc. 19th IEEE Int. Conf. Image Process., pp. 1477–1480, Sep./Oct. 2012.

7.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process., vol. 13, pp. 600-612, Apr. 2004. crossref(new window)

8.
Z. Wang and Q. Li, “Information content weighting for perceptual image quality assessment,” IEEE Trans. Image Process., vol. 20, no. 5, pp. 1185-1198, May 2011. crossref(new window)

9.
Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” Proc. 37th Asilomar Conf. Signals, Syst., Comput., pp. 1398–1402, Nov. 2003.

10.
S.-H. Bae and M. Kim, "A novel image quality assessment based on an adaptive feature for image characteristics and distortion types," IEEE Video Comm. and Image Proc., accepted for publication, Dec. 13-16, 2015.

11.
S.-H Bae and M. Kim,“A novel DCT-based JND model for luminance adaptation effect in DCT frequency,” IEEE Signal Process. Lett., vol. 20, no. 9, pp. 893-896, Sept. 2013. crossref(new window)

12.
Z. Wei and K. N. Ngan,“Spatio-temporal just noticeable distortion profile for grey scale image/video in DCT domain,” IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 3, pp. 337-346, Mar. 2009. crossref(new window)

13.
S.-H. Bae and M. Kim, “A new DCT-based JND model of monochrome images for contrast masking effects with texture complexity and frequency,” IEEE Int. Conf. on Image Proc, Melborne, Australia, Sept. 15-18, pp. 431-434, 2013.

14.
S.-H Bae and M. Kim, “A novel generalized DCT-based JND profile based on an elaborate CM-JND model for variable block-sized transforms in monochrome images,” IEEE Trans. on Image Process., vol. 23, no. 8, Aug. 2014.

15.
C.-H. Chou, Y.-C. Li, “A perceptually tuned subband image coder based on the measure of just-noticeable distortion profile,” IEEE Trans. Circuits Syst. Video Technol. vol. 5, no. 6, pp. 467-476, Dec. 1995. crossref(new window)

16.
T. Frese, C. A. Bouman, and J. P. Allebach. "A methodology for designing image similarity metrics based on human visual system models," Proc. SPIE, vol. 3016, pp. 472-483, 1997.

17.
N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008-A database for evaluation of full-reference visual quality assessment metrics,” Adv. Modern Radioelectron., vol. 10, pp. 30–45, 2009.

18.
Final Report From the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment VQEG. Available: http://www.vqeg.org, 2000.