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Speed up of Video Enhancement based on Human Perception

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Abstract

This paper presents SUVEHP (speed up of video enhancement based on human perception), a human perception-based model oriented to reduce the computational time of digital video restoration. In particular, two specific hypothesis tests able to classify degraded frame regions are proposed. Classification is performed in agreement with regions visual significance in order to enable or inhibit motion compensated enhancement. The level of the proposed hypothesis tests is theoretically assessed. Moreover, extensive experimental results on video sequences affected by additive Gaussian noise show that SUVEHP speeds up some standard motion compensated denoisers up to 60%, preserving or even slightly increasing both the objective and subjective visual quality of the restored sequences.

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References

  1. Liu, C., Freeman, W.T.: A high-quality video denoising algorithm based on reliable motion estimation. In: Proceedings of ECCV (2010)

  2. Dabov K., Foi A., Katkovnik V., Egiazarian K.: Image denoising by sparse 3d transform-domain collaborative filtering. In: IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    MathSciNet  Google Scholar 

  3. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Video denoising by sparse 3D transform-domain collaborative filtering. In: Proceedings of EUSIPCO 07 (2007, Sept)

  4. Winkler S.: Digital Video Quality—Vision Models and Metrics. Wiley, New York (2005)

    Book  Google Scholar 

  5. You J., Reiter U., Hannuksela M.M., Gabbouj M., Perkis A.: Perceptual-based quality assessment for audio-visual services: a survey. Signal Process. Image Commun. 25, 482–501 (2010)

    Article  Google Scholar 

  6. Hontsch I., Karam L.J.: Adaptive image coding with perceptual distortion control. In: IEEE Trans. Image Process. 11(3), 213–222 (2002)

    Google Scholar 

  7. Watson A.B., Yang G.Y., Solomon J.A., Villasenor J.: Visibility of wavelet quantization noise. In: IEEE Trans. Image Process. 6(8), 1164–1174 (1997)

    Google Scholar 

  8. Gutierrez J., Ferri F.J., Malo J.: Regularization operators for natural images based on nonlinear perception models. In: IEEE Trans. Image Process. 15(1), 189–200 (2006)

    MathSciNet  Google Scholar 

  9. Panetta K., Wharton E.J., Agaian S.S.: Human visual system-based image enhancement and logarithmic contrast measure. In: IEEE Trans. Syst. Man Cybern. 38(1), 174–188 (2008)

    Google Scholar 

  10. Bruni V., Vitulano D.: A generalized model for scratch detection. In: IEEE Trans. Image Process. 13(1), 44–50 (2004)

    Google Scholar 

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

    Google Scholar 

  12. Wang Z., Lu L., Bovik A.: Video quality assessment based on structural distortion measurement. Signal Process. Image Commun. 19(2), 121–132 (2004)

    Article  Google Scholar 

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

    Google Scholar 

  14. Imade, O.O., Chandler, D.M.: Image-adaptive contrast and entropy based model of regions of visible distortion. In: Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 65–68 (2010)

  15. Lewis A.S., Knowles G.: Image compression using the 2-d wavelet transform. In: IEEE Trans. Image Process. 1, 244–250 (1992)

    Google Scholar 

  16. Barni, M., Bartolini, F., Piva, A.: Improved wavelet-based watermarking through pixel-wise masking. In: IEEE Trans. Image Process. 10(5), 783–791 (2001, May)

  17. Watson, A.B., Borthowick, R., Taylor, M.: Image quality and entropy masking. Proc. SPIE 3016(2) (1997)

  18. Chandler D.M., Hemami S.S.: Dynamic contrast-based quantization for lossy wavelet image compression. In: IEEE Trans. Image Process. 14, 397–410 (2005)

    Google Scholar 

  19. Liu, Z., Karam, L.J., Watson, A.B.: JPEG2000 encoding with perceptual distortion control. In: IEEE Trans. Image Process. 15(7), 1763–1778 (2006, July)

  20. Podilchuk, C.I., Zeng, W.: Image-adaptive watermarking using visual models. In: IEEE J. Sel. Areas Commun. 16(4), 525–539 (1998, May)

  21. Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19(1), 011006-1/011006-21 (2010)

    Google Scholar 

  22. Wang Z., Li Q.: Video quality assessment using a statistical model of human visual speed perception. J. Opt. Soc. Am. A 24(12), B61–B69 (2007)

    Article  Google Scholar 

  23. Tong, H.H.Y., Venetsanopoulos, A.N.: A perceptual model for jpeg applications based on block classification, texture masking and luminance masking. In: Proceedings of ICIP’98, vol. 3 (1998)

  24. Zhang X., Lin W., Xue P.: Just-noticeable difference estimation with pixels in images. J. Vis. Commun. Image Represent. 19, 30–41 (2008)

    Article  Google Scholar 

  25. Boyce, J.M.: Noise Reduction of image sequences using adaptive motion compensated frame averaging. In: Proceedings of IEEE International Conference on ICASSP-92 (1992)

  26. Wong T.S., Bouman C.A., Pollak I., Fan Z.: A document image model and estimation algorithm for optimized JPEG decompression. In: IEEE Trans. Image Process. 18(11), 2518–2535 (2009)

    MathSciNet  Google Scholar 

  27. Monte, V., Frazor, R.A., Bonin, V., Geisler, W.S., Corandin, M.: Independence of luminance and contrast in natural scenes and in the early visual system. Nat. Neurosci. 8(12), 1690–1697 (2005, Dec)

    Google Scholar 

  28. Frazor R.A., Geisler W.S.: Local luminance and contrast in natural in natural images. Vis. Res. 46, 1585–1598 (2006)

    Article  Google Scholar 

  29. Casella G., Berger R.L.: Statistical Inference, Duxbury Advanced Series. Thomson Learning Inc., Pacific Grove (2002)

    Google Scholar 

  30. Bruni V., De Canditiis D., Vitulano D.: Human visual system for complexity reduction of image and video restoration. Lect. Notes Comput. Sci. 6855/2011, 261–268 (2011)

    Article  MathSciNet  Google Scholar 

  31. Cormen T.H., Leiserson C.E., Rivest R.L., Stein C.: Introduction to Algorithms, 2nd edn. McGraw-Hill, New York (2001)

    MATH  Google Scholar 

  32. Channabasappa M.N.: On the square root formula in the Bakhshali manuscript. Indian J. Hist. Sci. 11(2), 112–124 (1976)

    MathSciNet  MATH  Google Scholar 

  33. http://www.cipr.rpi.edu/resource/sequences/sif.html

  34. Shi Y.Q., Sun H.: Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms and Standards. CRC Press, Boca Raton (2000)

    Google Scholar 

  35. http://www.iac.cnr.it/~vitulano/suvehpweb/SUVEHP.htm

  36. Zhang L., Dong W., Zhang D., Shi G.: Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recogn. 43(4), 1531–1549 (2010)

    Article  MATH  Google Scholar 

  37. Kokaram A.: Motion Picture Restoration. Springer, Berlin (1998)

    Book  Google Scholar 

  38. Bankoski, J., Wilkins, P., Xu, Y.: Technical Overview of VP8, an Open Source Video Codec for the web. Google Int. Rep. (2011)

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

  40. Koenker R.: Quantile Regression. Cambridge University Press, Cambridge (2005)

    Book  MATH  Google Scholar 

  41. Tippet L.H.C.: On the extreme individuals and the range of samples taken from a normal population. Biometrika 17(3,4), 264–387 (1925)

    Google Scholar 

  42. Kutin, S.: Extensions to McDiarmid’s Inequality When Differences are Bounded with High Probability. Technical Report TR-2002-04, Department of Computer Science, University of Chicago (2002)

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Authors and Affiliations

  1. Department of SBAI, Faculty of Engineering, University of Rome “Sapienza”, Via A. Scarpa 16, 00161, Rome, Italy

    Vittoria Bruni

  2. Istituto per le Applicazioni del Calcolo “M. Picone”, C.N.R., Via dei Taurini 19, 00185, Rome, Italy

    Daniela De Canditiis & Domenico Vitulano

Authors
  1. Vittoria Bruni
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  2. Daniela De Canditiis
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  3. Domenico Vitulano
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Correspondence to Vittoria Bruni.

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Bruni, V., De Canditiis, D. & Vitulano, D. Speed up of Video Enhancement based on Human Perception. SIViP 8, 1199–1209 (2014). https://doi.org/10.1007/s11760-012-0344-y

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  • DOI: https://doi.org/10.1007/s11760-012-0344-y

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