Skip to main content
Log in

Novel distribution model of transformed coefficients in video coding using quad-tree structured block partitioning

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

Abstract

Today’s video coding standard such as high efficiency video coding uses a full quad-tree structured block partitioning, so the underlying statistics of transformed coefficients becomes more complicated to estimate than the previous standards due to the coding structure. However, a statistical distribution of transformed residue is important for a design of a smart encoder. Thus, in this paper, we present a theoretic analysis of a distribution of transformed coefficients produced from an encoder using different transform sizes, and derive a probability density function (pdf) for the estimation. The proposed density model provides a more accurate distribution model than the conventional pdfs. Parameters are theoretically estimated, and rate-distortion model is established from the proposed pdf. We also apply the proposed method to a rate control problem to show the efficiency of the proposed density model. Our experimental results show that the proposed method is better capable of modeling the mixed sources of multiple-type transform coefficients occurred from the quad-tree coding structure of transform and provides an accurate estimate in rate control.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Altunbasak, Y., & Kamaci, N. (2004). An analysis of the DCT coefficient distribution with H.264 video coder. In Proceedings of IEEE ICASSP (pp. 177–180).

  • Bilmes, J. A. (1998). A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden mixture models. Technical report, ICSI-TR-97-021, UC, Berkeley.

  • Chiang, T., & Zhang, Y. (1997). A new rate control scheme using quadratic rate control model. IEEE Transactions on Image Processing, 7, 246–250.

    Google Scholar 

  • Eude, T., Grisel, R., Cherifi, H., & Debrie, R. (1994). On the distribution of the DCT coefficients. In Proceedings of ICASSP (pp. 365–368).

  • Fan, Z., & Queiroz, R. (2000). Maximum likelihood estimation of JPEG quantization table in the identification of bitmap compression history. In Proceedings of IEEE ICIP (pp. 948–951).

  • Grimmett, G., & Stirzaker, D. (2001). Probability and random processes. Oxford: Oxford University Press.

    MATH  Google Scholar 

  • Hang, H.-M., & Chen, J.-J. (1997). Source model for transform video coder and its application-part i: Fundamental theory. IEEE Transactions on Image Processing, 7(2), 287–298.

    Google Scholar 

  • Kamaci, N., Altunbasak, Y., & Mersereau, R. (2005). Frame bit allocation for the H.264/AVC video coder via Cauchy-density-based rate and distortion models. IEEE Transactions on Image Processing, 15(8), 994–1006.

    Google Scholar 

  • Kang, J.-W., & Kim, C.-S. (2014). On DCT coefficient distribution in video coding using quad-tree structured partition. In Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA).

  • Kim, I.-K., Min, J., Lee, T., Han, W.-J., & Park, J. H. (2012). Block partitioning structure in the HEVC standard. IEEE Transactions on Circuits and Systems for Video Technology, 22(12), 1697–1706.

    Article  Google Scholar 

  • Kotz, S., Kozubowski, T., & Podgorski, K. (2001). The Laplace distribution and generalizations. Boston: Birkhauser.

    Book  MATH  Google Scholar 

  • Krupinski, R., & Purczynski, J. (2006). Approximated fast estimator for the shape parameter of generalized gaussian distribution. IEEE Transactions on Image Processing, 86(2), 205–211.

    MATH  Google Scholar 

  • Kwon, D.-K., Shen, M.-Y., & Kuo, C.-C. (2007). Rate control for H. 264 video with enhanced rate and distortion models. IEEE Transactions on Image Processing, 17(5), 517–529.

    Google Scholar 

  • Lam, E. Y., & Goodman, J. W. (2000). A mathematical analysis of the DCT coefficient distributions for images. IEEE Transactions on Image Processing, 9(10), 1661–1666.

    Article  MATH  Google Scholar 

  • Lee, B. S., & Kim, M. (2011). Modeling rates and distortions based on a mixture of Laplacian distributions for inter-predicted residues in quadtree coding of HEVC. IEEE Transactions on Image Processing, 18(10), 571–574.

    Google Scholar 

  • Li, B., Li, H., Li, L., & Zhang, J. (2012). JCTVC-K0103: Rate control by R-lambda model for HEVC. In ISO/IEC/JTC1/SC29/WG11 and ITU-T SG16 Q.6.

  • Li, X., Oertel, N., Hutter, A., & Kaup, A. (2009). Laplace distribution based lagrangian rate distortion optimization for hybrid video coding. IEEE Transactions on Image Processing, 19(2), 193–205.

    Google Scholar 

  • Ma, S., Gao, W., Wu, F., & Lu, Y. (2003). Rate control for advance video coding (avc) standard. In Proceedings of ICIP (pp. 793–796).

  • Ma, S., Gao, W., & Lu, Y. (2005). Rate-distortion analysis for H. 264/avc video coding and its application to rate control. IEEE Transactions on Image Processing, 15(12), 1533–1544.

    Google Scholar 

  • Muller, F. (1993). Distribution shape of two-dimensional DCT coefficients natural images. Electronics Letters, 29(22), 1935–1936.

    Article  Google Scholar 

  • Pan, F., Li, Z. G., Lim, K. P., Wu, D. J., & Yu, R. S. (2008). Adaptive rate control algorithm for low power video coding systems. IEEE Transactions on Image Processing, 18(1), 5–15.

    MATH  Google Scholar 

  • Pang, C., Au, O. C., Dai, J., & Zou, F. (2012). LMM-based frame-level rate control for H.264/AVC high-definition video coding. IEEE Transactions on Image Processing, 7, 737748.

    Google Scholar 

  • Rao, K. (2013). Video coding standards: AVS China, H.264/MPEG-4 part 10, HEVC, VP9, DIRAC and VC-1. In Proceedings Signal Processing: Algorithms, Architectures, Arrangements, and Applications (pp. 1–11).

  • Shishikui, Y., Matsuo, Y., Ichigaya, A., Iguchi, K., & Sasaida, S. (2010). Characteristics of super Hi-vision test sequences, document JCTVC-C0032. In ISO/IEC/JTC1/SC29/WG11 and ITU-T SG16 Q.6.

  • Sullivan, G. (1996). Efficient scalar quantization of exponential and laplacian random variables. IEEE Transactions on Image Processing, 42(5), 1365–1374.

    MATH  Google Scholar 

  • Winken, M., Helle, P., Marpe, D., Schwarz, H., & Wiegand, T. (2011). Transform coding in the HEVC test model. In Proceedings of ICIP (pp. 3693–3696).

  • Yilmaz, G. N. (2014). A bit rate adaptation model for 3d video. Multidimensional Systems and Signal Processing, 27(1), 1–15.

    MathSciNet  Google Scholar 

Download references

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2056587).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Je-Won Kang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kang, JW. Novel distribution model of transformed coefficients in video coding using quad-tree structured block partitioning. Multidim Syst Sign Process 28, 1589–1609 (2017). https://doi.org/10.1007/s11045-016-0438-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-016-0438-8

Keywords

Navigation