Skip to main content
SpringerLink
Log in

A low-complexity image compression approach with single spatial prediction mode and transform

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The well-known low-complexity JPEG and the newer JPEG-XR systems are based on block-based transform and simple transform-domain coefficient prediction algorithms. Higher complexity image compression algorithms, obtainable from intra-frame coding tools of video coders H.264 or HEVC, are based on multiple block-based spatial-domain prediction modes and transforms. This paper explores an alternative low-complexity image compression approach based on a single spatial-domain prediction mode and transform, which are designed based on a global image model. In our experiments, the proposed single-mode approach uses an average 20.5 % lower bit-rate than a standard low-complexity single-mode image coder that uses only conventional DC spatial prediction and 2-D DCT. It also does not suffer from blocking effects at low bit-rates.

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.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. JPEG-XR and Kodak test images were converted to YCbCr format with 420 sampling for the experiments.

  2. A Larger \(\rho \) value would model smoother blocks and a smaller \(\rho \) value detailed blocks better.

References

  1. Wallace, G.K.: The JPEG still picture compression standard. Commun. ACM 34(4), 30–44 (1991)

    Article  Google Scholar 

  2. Rabbani, M., Joshi, R.: An overview of the JPEG 2000 still image compression standard. Signal Process. Image Commun. 17(1), 3–48 (2002)

    Article  Google Scholar 

  3. Dufaux, F., Sullivan, G., Ebrahimi, T.: The JPEG XR image coding standard. IEEE Signal Process. Mag. 26, 195–199 (2009)

    Article  Google Scholar 

  4. Srinivasan, S., Tu, C., Regunathan, S.L., Sullivan, G.J.: Hd photo: a new image coding technology for digital photography. In: Tescher, A.G. (ed.) Optical Engineering+ Applications, pp. 66960A–66960A (2007)

  5. Pan, C.-H., Chien, C.-Y., Chao, W.-M., Huang, S.-C., Chen, L.-G.: Architecture design of full HD JPEG XR encoder for digital photography applications. Consum. Electron. IEEE Trans. 54(3), 963–971 (2008)

    Article  Google Scholar 

  6. De Simone, F., Goldmann, L., Baroncini, V., Ebrahimi, T.: Subjective evaluation of JPEG XR image compression. In: Tescher, A.G. (ed.) SPIE Optical Engineering+ Applications, pp. 74430L–74430L (2009)

  7. Wiegand, T., Sullivan, G.J., Bjontegaard, G., Luthra, A.: Overview of the H.264/AVC video coding standard. Circuits Syst. Video Technol. IEEE Trans. 13, 560–576 (2003)

    Article  Google Scholar 

  8. Lainema, J., Bossen, F., Han, W.J., Min, J., Ugur, K.: Intra coding of the HEVC standard. Circuits Syst. Video Technol. IEEE Trans. 22(12), 1792–1801 (2012)

    Article  Google Scholar 

  9. Huang, Y.-W., Hsieh, B.-Y., Chen, T.-C., Chen, L.-G.: Analysis, fast algorithm, and VLSI architecture design for H. 264/AVC intra frame coder. Circuits Syst. Video Technol. IEEE Trans. 15(3), 378–401 (2005)

    Article  Google Scholar 

  10. Lee, Y.-M., Sun, Y.-T., Lin, Y.: Satd-based intra mode decision for H.264/AVC video coding. Circuits Syst. Video Technol. IEEE Trans. 20(3), 463–469 (2010)

    Article  Google Scholar 

  11. Flickner, M.D., Ahmed, N.: A derivation for the discrete cosine transform. Proc. IEEE 70(9), 1132–1134 (1982)

    Article  Google Scholar 

  12. Kamisli, F.: Intra prediction based on Markov process modeling of images. Image Process. IEEE Trans. 22(10), 3916–3925 (2013)

    Article  MathSciNet  Google Scholar 

  13. Chen, Y., Han, J., Rose, K.: A recursive extrapolation approach to intra prediction in video coding. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pp. 1734–1738 (2013)

  14. Yeo, C., Tan, Y.H., Li, Z., Rahardja, S.: Mode-dependent transforms for coding directional intra prediction residuals. Circuits Syst. Video Technol. IEEE Trans. 22(4), 545–554 (2012)

    Article  Google Scholar 

  15. Jain, A.K.: A sinusoidal family of unitary transforms. Pattern Anal. Mach. Intell. IEEE Trans. PAMI–1(4), 356–365 (1979)

    Article  MATH  Google Scholar 

  16. Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. Comput. IEEE Trans. C–23(1), 90–93 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  17. Kamisli, F.: Block-based spatial prediction and transforms based on 2D Markov processes for image and video compression. Image Process. IEEE Trans. 24(4), 1247–1260 (2015)

    Article  MathSciNet  Google Scholar 

  18. Han, J., Saxena, A., Melkote, V., Rose, K.: Jointly optimized spatial prediction and block transform for video and image coding. Image Process. IEEE Trans. 21(4), 1874–1884 (2012)

    Article  MathSciNet  Google Scholar 

  19. Saxena, A., Fernandes, F.C.: DCT/DST-based transform coding for intra prediction in image/video coding. Image Process. IEEE Trans. 22(10), 3974–3981 (2013)

    Article  MathSciNet  Google Scholar 

  20. Malvar, H.S., Hallapuro, A., Karczewicz, K., Kerofsky, L.: Low-complexity transform and quantization in H.264/AVC. Circuits Syst. Video Technol. IEEE Trans. 13(7), 598–603 (2003)

    Article  Google Scholar 

  21. Jm reference software version 18.6. http://iphome.hhi.de/suehring/tml/ (2014)

  22. Sullivan, G.J., Wiegand, T.: Rate-distortion optimization for video compression. Signal Process. Mag. IEEE 15(6), 74–90 (1998)

    Article  Google Scholar 

  23. JPEG-XR image test set. http://documents.epfl.ch/groups/g/gr/gr-eb-unit/www/IQA/Original.zip (2015)

  24. Kodak image test set. http://r0k.us/graphics/kodak/ ( 2015)

  25. Bossen, F.: Common test conditions and software reference configurations. doc. jctvc-k1100. In: 11th Meeting: Joint Collaborative Team on Video Coding (JCT-VC) of ITU-T SG, vol. 16. (2013)

  26. Bjontegaard, G.: Calculation of average psnr differences between RD-curves. In: ITU-T SG16 VCEG-M33 (2001)

Download references

Author information

Authors and Affiliations

  1. Middle East Technical University, Ankara, Turkey

    Fatih Kamisli

Authors
  1. Fatih Kamisli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fatih Kamisli.

Additional information

This research was supported by Grant 113E516 of TÜBİTAK.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kamisli, F. A low-complexity image compression approach with single spatial prediction mode and transform. SIViP 10, 1409–1416 (2016). https://doi.org/10.1007/s11760-016-0908-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0908-3

Keywords

Search

Navigation