Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

FDQM: Fast Quality Metric for Depth Maps Without View Synthesis

Published: 01 July 2015 Publication History

Abstract

We propose a fast quality metric for depth maps, called fast depth quality metric (FDQM), which efficiently evaluates the impacts of depth map errors on the qualities of synthesized intermediate views in multiview video plus depth applications. In other words, the proposed FDQM assesses view synthesis distortions in the depth map domain, without performing the actual view synthesis. First, we estimate the distortions at pixel positions, which are specified by reference disparities and distorted disparities, respectively. Then, we integrate those pixel-wise distortions into an FDQM score by employing a spatial pooling scheme, which considers occlusion effects and the characteristics of human visual attention. As a benchmark of depth map quality assessment, we perform a subjective evaluation test for intermediate views, which are synthesized from compressed depth maps at various bitrates. We compare the subjective results with objective metric scores. Experimental results demonstrate that the proposed FDQM yields highly correlated scores to the subjective ones. Moreover, FDQM requires at least 10 times less computations than conventional quality metrics, since it does not perform the actual view synthesis.

References

[1]
M. Flierl and B. Girod, “Multiview video compression,” IEEE Signal Process. Mag., vol. 24, no. 6, pp. 66–76, Nov. 2007.
[2]
Multi-View Video Plus Depth (MVD) Format for Advanced 3D Video Systems, Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG document JVT-W100, Apr. 2007.
[3]
C. Fehn, “Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV,” Proc. SPIE, vol. 5291, pp. 93–104, May 2004.
[4]
W.-S. Kim, A. Ortega, P. Lai, D. Tian, and C. Gomila, “Depth map coding with distortion estimation of rendered view,” Proc. SPIE, vol. 7543, pp. 75430B-1–75430B-10, Jan. 2010.
[5]
Q. Zhang, P. An, Y. Zhang, and Z. Zhang, “Efficient rendering distortion estimation for depth map compression,” in Proc. 18th IEEE ICIP, Sep. 2011, pp. 1105–1108.
[6]
T.-Y. Chung, W.-D. Jang, and C.-S. Kim, “Efficient depth video coding based on view synthesis distortion estimation,” in Proc. IEEE VCIP, Nov. 2012, pp. 1–4.
[7]
T.-Y. Chung, J.-Y. Sim, and C.-S. Kim, “Bit allocation algorithm with novel view synthesis distortion model for multiview video plus depth coding,” IEEE Trans. Image Process., vol. 23, no. 8, pp. 3254–3267, Aug. 2014.
[8]
Z. Wang and A. C. Bovik, Modern Image Quality Assessment. San Rafael, CA, USA: Morgan & Claypool, 2006.
[9]
W. Lin and C.-C. J. Kuo, “Perceptual visual quality metrics: A survey,” J. Vis. Commun. Image Rerpresent., vol. 22, no. 4, pp. 297–312, May 2011.
[10]
L. Zhang, L. Zhang, X. Mou, and D. Zhang, “A comprehensive evaluation of full reference image quality assessment algorithms,” in Proc. 19th IEEE ICIP, Sep./Oct. 2012, pp. 1477–1480.
[11]
B. Girod, “What’s wrong with mean-squared error?” in Digital Images and Human Vision, A. B. Watson, Ed. Cambridge, MA, USA: MIT Press, 1993, pp. 207–220.
[12]
P. C. Teo and D. J. Heeger, “Perceptual image distortion,” Proc. SPIE, vol. 2179, pp. 127–141, May 1994.
[13]
Y.-K. Lai and C.-C. J. Kuo, “Image quality measurement using the Haar wavelet,” Proc. SPIE, vol. 3169, pp. 127–138, Oct. 1997.
[14]
N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. Image Process., vol. 9, no. 4, pp. 636–650, Apr. 2000.
[15]
Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett., vol. 9, no. 3, pp. 81–84, Mar. 2002.
[16]
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, Apr. 2004.
[17]
Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Proc. Conf. Rec. 37th IEEE Asilomar Conf. Signals, Syst., Comput., vol. 2. Nov. 2003, pp. 1398–1402.
[18]
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.
[19]
H. R. Sheikh, A. C. Bovik, and G. de Veciana, “An information fidelity criterion for image quality assessment using natural scene statistics,” IEEE Trans. Image Process., vol. 14, no. 12, pp. 2117–2128, Dec. 2005.
[20]
H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” IEEE Trans. Image Process., vol. 15, no. 2, pp. 430–444, Feb. 2006.
[21]
A. Shnayderman, A. Gusev, and A. M. Eskicioglu, “An SVD-based grayscale image quality measure for local and global assessment,” IEEE Trans. Image Process., vol. 15, no. 2, pp. 422–429, Feb. 2006.
[22]
N. Ponomarenko, F. Silvestri, K. Egiazarian, M. Carli, J. Astola, and V. Lukin, “On between-coefficient contrast masking of DCT basis functions,” in Proc. 3rd Int. Workshop Video Process. Quality Metrics Consum. Electron., 2007, pp. 1–4.
[23]
D. M. Chandler and S. S. Hemami, “VSNR: A wavelet-based visual signal-to-noise ratio for natural images,” IEEE Trans. Image Process., vol. 16, no. 9, pp. 2284–2298, Sep. 2007.
[24]
E. C. Larson and D. M. Chandler, “Most apparent distortion: Fullreference image quality assessment and the role of strategy,” J. Electron. Imag., vol. 19, no. 1, pp. 011006-1–011006-21, Jan. 2010.
[25]
L. Zhang, D. Zhang, X. Mou, and D. Zhang, “FSIM: A feature similarity index for image quality assessment,” IEEE Trans. Image Process., vol. 20, no. 8, pp. 2378–2386, Aug. 2011.
[26]
A. Liu, W. Lin, and M. Narwaria, “Image quality assessment based on gradient similarity,” IEEE Trans. Image Process., vol. 21, no. 4, pp. 1500–1512, Apr. 2012.
[27]
J. Wu, W. Lin, G. Shi, and A. Liu, “Perceptual quality metric with internal generative mechanism,” IEEE Trans. Image Process., vol. 22, no. 1, pp. 43–54, Jan. 2013.
[28]
W. Xue, L. Zhang, X. Mou, and A. C. Bovik, “Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,” IEEE Trans. Image Process., vol. 23, no. 2, pp. 684–695, Feb. 2014.
[29]
P.-H. Conze, P. Robert, and L. Morin, “Objective view synthesis quality assessment,” Proc. SPIE, vol. 8288, pp. 82881M-1–82881M-14, Feb. 2012.
[30]
A. Benoit, P. Le Callet, P. Campisi, and R. Cousseau, “Using disparity for quality assessment of stereoscopic images,” in Proc. 15th IEEE ICIP, Oct. 2008, pp. 389–392.
[31]
V. De Silva, H. K. Arachchi, E. Ekmekcioglu, and A. Kondoz, “Toward an impairment metric for stereoscopic video: A full-reference video quality metric to assess compressed stereoscopic video,” IEEE Trans. Image Process., vol. 22, no. 9, pp. 3392–3404, Sep. 2013.
[32]
C. T. E. R. Hewage, S. T. Worrall, S. Dogan, S. Villette, and A. M. Kondoz, “Quality evaluation of color plus depth map-based stereoscopic video,” IEEE J. Sel. Topics Signal Process., vol. 3, no. 2, pp. 304–318, Apr. 2009.
[33]
P. Joveluro, H. Malekmohamadi, W. A. C. Fernando, and A. M. Kondoz, “Perceptual video quality metric for 3D video quality assessment,” in Proc. 3DTV-Conf., True Vis.-Capture, Transmiss., Display 3D Video, Jun. 2010, pp. 1–4.
[34]
Reference Softwares for Depth Estimation and View Synthesis, ISO/IEC JTC1/SC29/WG11 document M15377, Apr. 2008.
[35]
Z. Wang and X. Shang, “Spatial pooling strategies for perceptual image quality assessment,” in Proc. IEEE ICIP, Oct. 2006, pp. 2945–2948.
[36]
A. K. Moorthy and A. C. Bovik, “Visual importance pooling for image quality assessment,” IEEE J. Sel. Topics Signal Process., vol. 3, no. 2, pp. 193–201, Apr. 2009.
[37]
Reference Software for Multiview Video Coding, ISO/IEC JTC1/SC29/WG11 document N10704, Jul. 2009.
[38]
Methodology for the Subjective Assessment of the Quality of Television Pictures, ITU-R Rec. document BT.500-13, Jan. 2012.
[39]
A. M. van Dijk, J.-B. Martens, and A. B. Watson, “Quality asessment of coded images using numerical category scaling,” Proc. SPIE, vol. 2451, pp. 90–101, Feb. 1995.
[40]
H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440–3451, Nov. 2006.
[41]
R. A. Fisher, Statistical Methods for Research Workers. London, U.K.: Oliver & Boyd, 1925.

Index Terms

  1. FDQM: Fast Quality Metric for Depth Maps Without View Synthesis
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image IEEE Transactions on Circuits and Systems for Video Technology
      IEEE Transactions on Circuits and Systems for Video Technology  Volume 25, Issue 7
      July 2015
      177 pages

      Publisher

      IEEE Press

      Publication History

      Published: 01 July 2015

      Author Tags

      1. virtual view synthesis
      2. 3-D video
      3. depth map quality assessment
      4. image quality assessment
      5. multiview video plus depth (MVD)
      6. spatial pooling

      Qualifiers

      • Research-article

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 0
        Total Downloads
      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 25 Nov 2024

      Other Metrics

      Citations

      View Options

      View options

      Login options

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media