Circuits, Systems, and Signal Processing

, Volume 37, Issue 8, pp 3537–3557 | Cite as

Block Matching Video Compression Based on Sparse Representation and Dictionary Learning

  • Maziar Irannejad
  • Homayoun Mahdavi-NasabEmail author


This work presents a video compression method based on sparse representation and dictionary learning algorithms. The proposed scheme achieves superb rate-distortion performance and decent subjective quality, compared to modern standards, especially at low bit-rates. Different from similar works, sparse representation is employed here for both intra-frame and block matching inter-frame motion information. Dividing video frames to reference and current frames, motion vectors and motion compensation residuals of current frames are estimated in regard to reference frames. The sparse codes of reference frames and motion compensation residuals are obtained using learned dictionaries, entropy-coded, and stored or sent to the receiver along with the coded motion field. In the receiver, after decoding the sparse codes and motion vectors, the reference frames and residuals are reconstructed employing the same learned dictionary and the current frames are recovered using the reference frames and motion fields. In the proposed scheme, the Iterative Least Square Dictionary Learning Algorithm (ILS-DLA) and K-SVD dictionary building methods are employed in the DCT domain. The compression rate and quality of the method based on the two dictionary learning algorithms are compared to each other and to H.264/AVC and HEVC modern standards. The results based on PSNR and SSIM criteria show that the proposed approach presents superior performance respect to H.264/AVC and even HEVC for higher bit-rates of QCIF video format, and the K-SVD learning algorithm performs slightly better than the ILS-DLA for the purpose.


Sparse representation Dictionary learning K-SVD ILS-DLA Block matching 


  1. 1.
    M. Aharon, M. Elad, A. Bruckstein, K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)CrossRefzbMATHGoogle Scholar
  2. 2.
    S. Becker, J. Bobin, E.J. Candes, NESTA: a fast and accurate first-order method for sparse recovery. SIAM J. Imaging Sci. 4(1), 1–39 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    S. Becker, E.J. Candes, M. Grant, Templates for convex cone problems with applications to sparse signal recovery. Math. Prog. Comp. 3(3), 165–218 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    T. Blumensath, M. Davies, Iterative hard thresholding for compressed sensing. Appl. Comput. Harmon. Anal. 27(3), 265–274 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    O. Bryt, M. Elad, Compression of facial images using the K-SVD algorithm. J. Vis. Commun. Image R. 19(4), 270–283 (2008)CrossRefGoogle Scholar
  6. 6.
    E.J. Candes, M.B. Wakin, An introduction to compressive sampling. IEEE Signal. Process. Mag. 25(2), 21–30 (2008)CrossRefGoogle Scholar
  7. 7.
    E.J. Candes, M.B. Wakin, S. Boyd, Enhancing sparsity by reweighted 1 minimization. J. Fourier Anal. Appl. 14(5), 877–905 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    K.Y. Chang, C.F. Lin, C.S. Chen, Y.P. Hung, Single-pass K-SVD for efficient dictionary learning. Circuits. Syst. signal Process. 33(1), 309–320 (2014)CrossRefGoogle Scholar
  9. 9.
    R. Chartrand, Exact reconstruction of sparse signals via nonconvex minimization. IEEE Signal. Procss. Lett. 14, 707–710 (2007)CrossRefGoogle Scholar
  10. 10.
    S. Chen, S.A. Billings, W. Luo, Orthogonal least squares methods and their application to non-linear system identification. Int. J. Control. 50(5), 1873–1896 (1989)CrossRefzbMATHGoogle Scholar
  11. 11.
    S.F. Cotter, B.D. Rao, K. Engan, K. Kreutz-Delgado, Sparse solutions to linear inverse problems with multiple measurement vectors. IEEE Trans. Signal Process. 53(7), 2477–2488 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    M.E. Davies, Y.C. Eldar, Rank awareness in joint sparse recovery. IEEE Trans. Inf. Theory. 58(2), 1135–1146 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    D. Donoho, Compressed sensing. IEEE Trans. Inf. Theory. 52(4), 1289–1306 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    D.L. Donoho, A. Maliki, A. Montanari, Message-passing algorithms for compressed sensing. Proc. Natl. Acad. Sci. 106(45), 18914–18919 (2009)CrossRefGoogle Scholar
  15. 15.
    Y.C. Eldar, G. Kutyniok, Theory and Applications, Compressed sensing (Cambridge University Press, New York, 2012)Google Scholar
  16. 16.
    K. Engan, K. Skretting, J.H. Husy, Family of iterative LS-based dictionary learning algorithms, ILS-DLA, for sparse signal representation. Dig. Signal Process. 17(1), 32–49 (2007)CrossRefGoogle Scholar
  17. 17.
    M.A.T. Figueiredo, R.D. Nowak, S.J. Wright, Gradient projection for sparse reconstruction application to compressed sensing and other inverse problems. IEEE J. Sel. Topics Sig. Process. 1(4), 586–597 (2007)CrossRefGoogle Scholar
  18. 18.
    M. Hugel, H. Rauhut, T. Strohmer, Remote sensing via 1 minimization. Found. Comput. Math. 14(1), 115–150 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    J.R. Jain, A.K. Jain, Displacement measurement and its application to interframe image coding. IEEE Trans. Comm. 29(12), 1799–1808 (1981)CrossRefGoogle Scholar
  20. 20.
    X.X. Ji, G. Zhang, An adaptive SAR image compression method. Comp. Electr. Eng. 62(8), 473–484 (2017)Google Scholar
  21. 21.
    W. Lin, K. Panusopone, D. Baylon, M.T. Sun, A computation control motion estimation method for complexity scalable video coding. IEEE Trans. Circuits Syst. Video Technol. 20(11), 1533–1543 (2010)CrossRefGoogle Scholar
  22. 22.
    W. Lin, K. Panusopone, D. Baylon, M.T. Sun, Z. Chen, H. Li, A fast sub-pixel motion estimation algorithm for H.264/AVC video coding. IEEE Trans. Circuits Syst. Video Technol. 21(2), 237–242 (2011)CrossRefGoogle Scholar
  23. 23.
    W. Lin, M.T. Sun, H. Li, Z. Chen, W. Li, B. Zhou, Macroblock classification for video applications involving motions. IEEE Trans. Broadcast. 58(1), 34–46 (2012)CrossRefGoogle Scholar
  24. 24.
    H. Mahdavi-Nasab, S. Kasaei, New half pixel accuracy motion estimation algorithms for low bitrate video communicatons. Scientia Iranica 15(6), 507–516 (2008)Google Scholar
  25. 25.
    D. Needell, J. Tropp, COSAMP: iterative signal recovery from incomplete and inaccurate samples. App. Comput. Harmon. Anal. 26, 301–321 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Y.C. Pati, R. Rezaifar, P.S. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, in Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, (1993), pp. 40–44Google Scholar
  27. 27.
    R. Rubinstein, A.M. Bruckstein, M. Elad, Dictionaries for sparse representation modeling. Proc. IEEE. 98(6), 1045–1057 (2010)CrossRefGoogle Scholar
  28. 28.
    K. Skretting, K. Engan, Image compression using learned dictionaries by RLS-DLA and compared with K-SVD, in Proceedings of the IEEE ICASSP, (2011), pp. 1517–1520Google Scholar
  29. 29.
    P. Stoica, A. Nehorai, MUSIC, maximum likelihood, and Cramer-Rao bound. IEEE Trans. Acoust. Speech Sig. Proc. 37, 720–741 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    G.J. Sullivan, J. Ohm, W.J. Han, T. Wiegand, Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2012)CrossRefGoogle Scholar
  31. 31.
    Y. Sun, M. Xu, X. Tao, J. Lu, Online dictionary learning based intra-frame video coding. Wireless Pers. Commun. 74, 1281–1295 (2014)CrossRefGoogle Scholar
  32. 32.
    A.M. Taheri, H. Mahdavi-Nasab, Facial image compression using adaptive multiple dictionaries, in 9th Iranian Conference on Machine Vision and Image Processing, (2015), pp. 92–95Google Scholar
  33. 33.
    K.S. Thyagarajan, Still image and video compression with MATLAB (Wiley, New Jersey, 2010)CrossRefGoogle Scholar
  34. 34.
    I. Tosic, P. Frossard, Dictionary learning. Signal Process. Mag. IEEE 28(2), 27–38 (2011)CrossRefzbMATHGoogle Scholar
  35. 35.
    J.A. Tropp, Greed is good: algorithmic results for sparse approximation. IEEE Trans. Inf. Theory 50(10), 2231–2242 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    J.A. Tropp, S.J. Wright, Computational methods for sparse solution of linear inverse problems. Proc. IEEE. 98(6), 948–958 (2010)CrossRefGoogle Scholar
  37. 37.
    H.L. Van Trees, Detection, estimation and modulation theory. Optimum array processing (Wiley, New York, 2002)Google Scholar
  38. 38.
    Z. Wang, A. Bovik, H.R. Sheikh, E.P. Simoncelli, Image qualifty assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  39. 39.
    T. Wiegand, G. Sullivan, G. Bjontegaard, A. Luthra, Overview of the H.264/AVC video coding standard. IEEE Trans. Circuits Syst. Video Technol. 13, 560–576 (2003)CrossRefGoogle Scholar
  40. 40.
    D. Wipf, S. Nagarajan, Iterative reweighted 1 and 2 methods for finding sparse solutions. IEEE J. Sel. Topics Signal Process. 4(2), 317–329 (2010)CrossRefGoogle Scholar
  41. 41.
    H. Xiong, Z. Pan, X. Ye, C.W. Chen, Sparse spatio-temporal representation adaptive regularized dictionary learning for low bit-rate video coding. IEEE Trans. Circuits Syst. Video Technol. 23(4), 710–728 (2013)CrossRefGoogle Scholar
  42. 42.
    X. Zhan, R. Zhang, D. Yin, C. Huo, SAR image compression using multiscale dictionary learning and sparse representation. Remote Sens. Lett. 10(5), 1090–1094 (2013)CrossRefGoogle Scholar
  43. 43.
    J.Y. Zhu, Z.Y. Wang, R. Zhong, S.M. Qu, Dictionary based surveillance image compression. J. Vis. Commun. Image R. 31, 225–230 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Digital Processing and Machine Vision Research Center, Najafabad BranchIslamic Azad UniversityNajafabadIran
  2. 2.Department of Electrical Engineering, Najafabad BranchIslamic Azad UniversityNajafabadIran

Personalised recommendations