Skip to main content
SpringerLink
Log in

Intra frame compression and video restoration based on conditional markov processes theory

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

Digital images and video have significant statistical redundancy due to high intra and inter-frame correlation. Modeling images and video using two-dimensional Markov processes with discrete states maximizes the use of images and video redundancy while processing. Intra-frame compression algorithm proposed below allows 2–3 times increase in processing speed for comparable compression ratios. Nonlinear filtering algorithm of video (distorted during transmission on a radio channel in terms of interference) allows effective video restoring even for negative signal/noise ratio unlike the known methods. A distinctive feature of this algorithm is ease of implementation with minimal computing resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  • Asif, M. S., Fernandes, F., & Romberg, J. (2013). Low-complexity video compression and compressive sensing. In Signals, Systems and Computers, 2013 Asilomar Conference on, pp.579–583, 3-6 Nov. 2013.

  • Chan, T.-W. (2005). A novel content-adaptive video denoising filter, (ICASSP ’05). IEEE Acoustics, Speech, and Signal Processing, 2, 18–23.

    Google Scholar 

  • Chen, C. H., & Wang, X. (2004). Speckle reduction and edge enhancement of NDE C-Scan images using ICA. Review of Quantitative Nondestructive Evaluation, 23, 573–580.

    Article  Google Scholar 

  • Chia-Hung, Y., Shu-Jhen, F., Jiang, C.-Y., Lin, P.-L., & Suei, M.-K. C. (2015). Chang a new intra prediction with adaptive template matching through finite state machine. Journal of Visual Communication and Image Representation, 29, 33–45.

    Article  Google Scholar 

  • Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Image denoising by sparse 3-D transform- domain collaborative filtering. IEEE Transactions on Image Processing, 16(8), 2080–2095.

    Article  MathSciNet  Google Scholar 

  • Gruzman I. S., Kirichuk V. S., Kosykh V. P., Peretyagin G. I., & Spektor A. A. (2000). Digital image processing in the information systems: Tutorial. Novosibirsk, Russia: NGTU Publishing. 168p.

  • Habibi, A. (1972). Two-dimensional Bayesian estimate of images. Proceedings of the IEEE, 60, 878–883.

    Article  Google Scholar 

  • Happonen, A. P., & Koskinen, M. O. (2007). Experimental investigation of angular stackgram filtering for noise reduction of SPECT projection data: study with linear and nonlinear filters. International Journal of Biomedical Imaging, 2007, 1–12. doi:10.1155/2007/38516.

  • Kamisli, F. (2015). Block-based spatial prediction and transforms based on 2D Markov processes for image and video compression, IEEE Transactions-on image processing, vol 24, No 4, 2015.

  • Kang, L.-W., Chun-Shien, L., & Lin, C.-Y. (2012). Low-complexity video coding via power-rate-distortion optimization. Journal of Visual Communication and Image Representation, 23, 569–585.

    Article  Google Scholar 

  • Kervrann, C., & Boulanger, J. (2008). Local adaptivity to variable smoothness for exemplar-based image denoising and representation. International Journal of Computer Vision, 79, 45.

    Article  Google Scholar 

  • Kravchenko, V. F., Ponomarev, V. I., & Pustovoit, V. I. (2008). Suppression of pulse noise in multi-channel images on the basis of the theory of indistinct sets and angular divergences of pixels. Proceedings of Academy of Sciences, 423(1), 39–43.

    Google Scholar 

  • Mahmoudi, M., & Sapiro, G. (2005). Fast image and video denoising via nonlocal means of similar neighborhoods., IEEE, Dec. 2005.

  • Ohm, J.-R., Sullivan, G. J., Schwarz, H., Tan, T. K., & Wiegand, T. (2012). Comparison of the coding efficiency of video coding standards—including high efficiency video coding (HEVC). IEEE Transaction Circuits Systems Video Technology, 22(12), 1669–1684.

    Article  Google Scholar 

  • Petrov, E. P., Trubin, I. S., Medvedeva, E. V., & Smolskiy, S. M. (2013). Development of nonlinear filtering algorithms of digital half-tone images// integrated models for information communication systems and net-works : Design and development / Aderemi A. Atayero and Oleg I. Sheluhin, editors, IGI Global. pp.278-304.

  • Petrov, E. P, Trubin, I. S., Medvedeva, E. V., & Smolskiy, S. M. (2013). Mathematical models of video-sequences of digital half-tone images // integrated models for information communication systems and net-works : Design and development / Aderemi A. Atayero and Oleg I. Sheluhin, editors, IGI Global. pp.207–241.

  • Petrov, E. P., & Medvedeva, E. V. (2010). Nonlinear filtering of statistically connected video-sequences of digital halftone images. Radio Engineering and Electronics, 55(3), 330–339.

    Google Scholar 

  • Petrov, E. P., & Medvedeva, E. V. (2010). Nonlinear filtering of statistically connected video sequences based on hidden markov chains. Journal of Communications Technology and Electronics, 55(3), 307–315.

    Article  Google Scholar 

  • Petrov, E. P., Medvedeva, E. V., & Metelev, A. P. (2010). Method of combined non-linear filtrering of correlated video images. Nonlinear World, 11, 677–684.

    Google Scholar 

  • Petrov, E. P., Trubin, I. S., & Tikhonov, I. E. (2003). Nonlinear Digital Filtrering of Halftone Images. Radio Engineering, 5, 7–10.

    Google Scholar 

  • Samoilin, E. A. (2009). Method of the asymmetrical noiseproof adaptation of an aperture in problems of a nonparametric image filtration. Radio Engineering and Eectronics, 54(7), 842–852.

    Google Scholar 

  • Shuang, Y., Qiao, F., Yang, X., Luo, L., & Yang, H. (2014). A survey on low-complexity video coding methods and architectures. Recent Advances in Electrical & Electronic Engineering, 7, 26–36.

    Google Scholar 

  • Stan, Z. L. (2009). Markov random field modeling in image analysis. Berlin: Springer London Limited.

    MATH  Google Scholar 

  • Stan, Z., & Li, M. (2009). Random field modeling in image analysis. Berlin: Springer. p. 357.

    MATH  Google Scholar 

  • Sze, V., Budagavi, M., & Sullivan, G. (2014). High efficiency video coding (HEVC): Algorithms and architectures (integrated circuits and systems). Berlin: Springer.

    Book  Google Scholar 

  • Tchobanou, M. (2009). Multidimensional multirate systems of signal processing. Moscow, Russia: Technosphera. 480 p.

  • Vasilyev K. K., & Krasheninnikov V. R. (2007). Statistical analysis of multy-dimensional images. Ulyanovsk, Russia: UlGTU. p.170.

  • Vinkler, G. (2002). Analysis of images, casual fields and dynamic monte-carlo methods. mathematical bases. Novosibirsk, Russia: SO RAN Publishing.

    Google Scholar 

  • Wai-Ki, C., & Michael, K. (2006). Ng Markov chains: Models, algorithms and applications. Berlin: Springer Science+Business Media.

    MATH  Google Scholar 

  • Wien, M. (2015). High Efficiency video coding: Coding tools and specification. Berlin: Springer. 314 p.

    Book  Google Scholar 

  • Xu, X. (2004). Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. In IEEE Trans Image Process Feb. 2004.

Download references

Author information

Authors and Affiliations

  1. Vyatka State University, Kirov, Russia

    E. P. Petrov, E. V. Medvedeva & N. L. Kharina

  2. National Research University “MPEI”, Moscow, Russia

    M. K. Tchobanou

Authors
  1. E. P. Petrov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. V. Medvedeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. L. Kharina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. K. Tchobanou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. K. Tchobanou.

Ethics declarations

Conflict of interest

The authors confirm that there is no conflict of financial and non-financial interests.

Ethical Statement

The authors confirm that there is no conflict of financial and non-financial interests. This work was performed within the basic part of the government order of the Ministry of Education and Science of the Russian Federation (the Item 2014/61 “Image processing techniques in video information systems with limited computing resources”).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Petrov, E.P., Medvedeva, E.V., Kharina, N.L. et al. Intra frame compression and video restoration based on conditional markov processes theory. Multidim Syst Sign Process 27, 719–742 (2016). https://doi.org/10.1007/s11045-016-0394-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-016-0394-3

Keywords

Search

Navigation