Journal of Real-Time Image Processing

, Volume 5, Issue 4, pp 259–274 | Cite as

DCT-domain coder for digital video applications

  • Evgeny KaminskyEmail author
  • Alex Ginzburg
  • Ofer Hadar
Special Issue


In this paper, we present an effective DCT-domain video encoder architecture that decreases the computational complexity of conventional hybrid video encoders by reducing the number of transform operations between the pixel and the DCT domains. The fixed video encoder architecture (such as a fixed DCT block of 8 × 8 size) and a huge number of DCT/IDCT transforms performed during the video encoding process limit the minimum possible computational load of conventional video encoders. In this study, we solve this problem by developing a flexible video encoder architecture, which reduces video encoder computational complexity by performing low-resolution coarse-step motion estimation operations in the DCT domain. When a high level of motion activity is detected, the video encoder slightly increases the computational complexity of the motion estimation operation by computing fine-search block matching for a small-size search window in a reference frame. The proposed DCT-domain video encoder architecture is based on the conventional hybrid coder and on a set of fast integer composition and decomposition DCT transforms. The set of transforms implements a technique for estimation of DCT coefficients of a block that is partitioned by the sub-blocks. Experimental results of this method were compared with the results of the conventional hybrid coder in terms of PSNR quality and computational complexity. This comparison shows that the computational complexity of the proposed encoder is lower by 26.8% with respect to the conventional hybrid video coder for the same objective PSNR quality.


Video compression Computational complexity Complexity reduction Discrete cosine transform (DCT) DCT-domain video encoder DCT-domain motion estimation MPEG-2 


  1. 1.
    Parhi, K.K., Nishitani T.: Digital Signal Processing for Multimedia Systems, pp. 355–369. Marcel Dekker, Inc. (1999)Google Scholar
  2. 2.
    Loeffler, C., Ligtenberg, A., Moschytz, G.S.: Practical fast 1-D DCT algorithms with 11 multiplications. In: ICASSP-89, pp. 988–991 (1989)Google Scholar
  3. 3.
    Kamangar, F.A., Rao, K.R.: Fast algorithms for the 2-D discrete cosine transform. IEEE Trans. Comput. 31(9), 899–906 (1982)zbMATHCrossRefGoogle Scholar
  4. 4.
    Cho, N.I., Lee, S.U.: Fast algorithm and implementation of 2-D discrete cosine transform. IEEE Trans. Signal Proc. 38(3), 297–305 (1991)Google Scholar
  5. 5.
    Feig, E., Winograd, S.: Fast algorithms for the discrete cosine transform. IEEE Trans. Signal Proc. 40(9), 2174–2193 (1992)zbMATHCrossRefGoogle Scholar
  6. 6.
    Girod, B., Stuhlmüller, K.W.: A content-dependent fast DCT for low bit-rate video coding. In: Proc. ICIP’98, pp. 80–84 (1998)Google Scholar
  7. 7.
    Pao, I.-M., Sun, M.-T.: Modeling DCT coefficients for fast video encoding. IEEE Trans. Circuits Syst. Video Technol. 9, 608–616 (1999)CrossRefGoogle Scholar
  8. 8.
    Docef, A., Kossentini, F., Nguuyen-Phi, K., Ismaeil, I.R.: The quantized DCT and its application to DCT-based video coding. IEEE Trans. Image Process. 11, 177–187 (2002)CrossRefGoogle Scholar
  9. 9.
    Lengwehasatit, K., Ortega, A.: DCT computation based on variable complexity fast approximations. Presented at the ICIP’98. Chicago, Oct 1998Google Scholar
  10. 10.
    Lengwehasatit, K., Ortega, A.: Scalable variable complexity approximate forward DCT. Trans. Circuits Syst. Video Technol. 14, 1236–1248 (2004)CrossRefGoogle Scholar
  11. 11.
    Chang, S.-F., Messerschmitt, D.G.: A new approach to decoding and compositing motion-compensated DCT based images. In: Proceedings of ICASSP’93, pp. V.421–V.424. Minneapolis, Apr 1993Google Scholar
  12. 12.
    Smith, B.C., Rowe, L.: Algorithms for manipulating compressed images. IEEE Comput. Graph. Appl. 13(5), 34–42 (1993)CrossRefGoogle Scholar
  13. 13.
    Chang, S.-F., Messerschmitt, D.G.: Manipulation and compositing of MC-DCT compressed video. IEEE J. Select. Areas Commun. 13(1, Pt. 2), 1–11 (1995)CrossRefGoogle Scholar
  14. 14.
    Chitprasert, B., Rao, K.R.: Discrete cosine transforms filtering. Signal Process. 19, 233–245 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Arman, F., Hsu, A. Chiu, M.Y.: Feature management for large video databases. In: Storage and Retrieval for Image and Video Databases, vol. SPIE-1908, pp. 2–12 (1993)Google Scholar
  16. 16.
    Meng, J., Juan, Y., Chang, S.; Scene change detection in a MPEG compressed video sequence. In: SPIE Symposium on Electronic Imaging: Science and Technology-Digital Video Compression: Algorithm and Technology. San Jose, Feb 1995Google Scholar
  17. 17.
    Malvar, H.S., Hallapuro, A., Karczewicz, M., Kerofsky, L.: Low-complexity transform and quantization in H.264/AVC. IEEE Trans. Circuits Syst. Video Technol. 13(7), 598–603 (2003)Google Scholar
  18. 18.
    Jiang, J., Feng, G.: The spatial relationship of DCT coefficients between a block and its sub-blocks. IEEE Trans. Signal Process. 50(5), 1160–1169 (2002)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Hallapuro, A., Karczewicz, M.: Low complexity transform and quantization—Part I: basic implementation. In: Joint Video Team (JVT) of ISO/IEC MPEG & ITU-T VCEG (ISO/IEC JTC1/SC29/WG11 and ITU-T SG16 Q.6), 2nd Meeting, Document: JVT-B038. Geneva, Jan 29–Feb 1, 2002Google Scholar
  20. 20.
    Kuhn, P.: Algorithms, Complexity Analysis and VLSI Architectures for MPEG-4 Motion Estimation, pp. 29–33. Kluwer Academic Publishers, Boston (1999)zbMATHGoogle Scholar
  21. 21.
    Rao, K.R., Yip, P.: Discrete Cosine Transform: Algorithms, Advantages, Applications, Chap. 4. Academic Press, Boston (1990)Google Scholar
  22. 22.
    Liu, L., Tran, T.D.: An 8 × 8 IEEE-compliant lifting-based multiplierless IDCT structure and algorithm. In: IEEE Acoustics, Speech and Signal Processing. Honolulu, Apr 2007Google Scholar
  23. 23.
    Ma, K.K., Housr, P.I., Huang, L.: Status report of core experiment on fast block matching motion estimation. In: ISO/IEC JTC1/SC29/WG11, MPEG98/M3299. Tokyo, March 1998Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringBen-Gurion University of the NegevBeershebaIsrael
  2. 2.Electro-Optics UnitBen-Gurion University of the NegevBeershebaIsrael
  3. 3.Department of Communication Systems EngineeringBen-Gurion University of the NegevBeershebaIsrael

Personalised recommendations