Skip to main content
Log in

High efficiency lossless image recompression algorithm with asymmetric numeral systems for real-time mobile application

  • Research
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

JPEG images are widely used by individual users, data centers, cloud storages, and network file systems. The huge transmission and storage bandwidth of existing JPEG images is becoming a big challenge due to its low compression efficiency. Some methods such as Google’s Brunsli can further compress these JPEG images adequately and restore them back to JPEG format losslessly when needed, but its decoding speed is not fast enough especially for real-time mobile applications. To further reduce its decoding complexity, we proposed three optimizations based on Brunsli, which include using asymmetric numeral systems coding to replace the existing arithmetic coding, designing the joint encoding of multiple symbols and cache-friendly optimizing the data structures. Experimental results demonstrate that compared with Brunsli, the proposed method achieves average 1.86–2.25 times decoding faster on mobile platform with comparable compression efficiency which is really valuable for power sensitive real-time mobile applications.

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
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

All data included in this study are available upon request by contact with the corresponding author. The source code is not publicly available due to privacy.

References

  1. Cisco. Cisco annual internet report (2018-2023) white paper. https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html (2020)

  2. Rahman, M.A., Hamada, M., Shin, J.: The impact of state-of-the-art techniques for lossless still image compression. Electronics 10(3), 360 (2021)

    Article  Google Scholar 

  3. Horn, D.R., Elkabany, K., Lesniewski-Lass, C., Winstein, K.: The design, implementation, and deployment of a system to transparently compress hundreds of petabytes of image files for a \(\{\)File-Storage\(\}\) service. In: 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17) , pp. 1–15 (2017)

  4. Pan, W., Li, Z., Zhang, Y., Weng, C.: The new hardware development trend and the challenges in data management and analysis. Data Sci. Eng. 3(3), 263 (2018)

    Article  Google Scholar 

  5. Subramanya, A.: Image compression technique. IEEE Potentials 20(1), 19 (2001)

    Article  Google Scholar 

  6. Yang, M., Bourbakis, N.: An overview of lossless digital image compression techniques. In: 48th Midwest Symposium on circuits and systems, pp. 1099–1102. IEEE (2005)

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

    Article  Google Scholar 

  8. Dufaux, F., Sullivan, G.J., Ebrahimi, T.: The JPEG XR image coding standard [Standards in a Nutshell]. IEEE Signal Process. Mag. 26(6), 195 (2009)

    Article  Google Scholar 

  9. Taubman, D.S., Marcellin, M.W.: JPEG2000: standard for interactive imaging. Proc. IEEE 90(8), 1336 (2002)

    Article  Google Scholar 

  10. Li, Z., Li, J., Ren, A., Cai, R., Ding, C., Qian, X., Draper, J., Yuan, B., Tang, J., Qiu, Q., et al.: HEIF: Highly efficient stochastic computing-based inference framework for deep neural networks. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 38(8), 1543 (2018)

    Article  Google Scholar 

  11. Bross, B., Chen, J., Ohm, J.R., Sullivan, G.J., Wang, Y.K.: Developments in international video coding standardization after avc, with an overview of versatile video coding (vvc). Proc. IEEE 109(9), 1463 (2021)

    Article  Google Scholar 

  12. Krishnaraj, N., Elhoseny, M., Thenmozhi, M., Selim, M.M., Shankar, K.: Deep learning model for real-time image compression in Internet of Underwater Things (IoUT). J. Real-Time Image Process. 17(6), 2097 (2020)

    Article  Google Scholar 

  13. Cheng, Z., Sun, H., Takeuchi, M., Katto, J.: Deep residual learning for image compression, In: CVPR Workshops (2019)

  14. Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., Van Gool, L.: Conditional probability models for deep image compression, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4394–4402 (2018)

  15. Li, M., Zuo, W., Gu, S., Zhao, D., Zhang, D.: in Learning convolutional networks for content-weighted image compression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3214–3223 (2018)

  16. Lakhani, G.: DCT coefficient prediction for JPEG image coding. In: 2007 IEEE international conference on image processing, vol. 4, pp. IV–189. IEEE (2007)

  17. Alakuijala, J., Boukortt, S., Ebrahimi, T., Kliuchnikov, E., Sneyers, J., Upenik, E., Vandevenne, L, Versari, L., Wassenberg, J.: Benchmarking JPEG XL image compression. In: Optics, photonics and digital technologies for imaging applications VI, vol. 11353, pp. 187–206. SPIE (2020)

  18. Alakuijala, J., Van Asseldonk, R., Boukortt, S., Bruse, M., Comşa, I.M., Firsching, M., Fischbacher, T., Kliuchnikov, E., Gomez, S., Obryk, R.: et al.,Benchmarking JPEG XL image compression. In: JPEG XL next-generation image compression architecture and coding tools applications of digital image processing XLII, vol. 11137, pp. 112–124. SPIE (2019)

  19. Brunsli. A lossless JPEG repacking library. https://github.com/google/brunsli (2021)

  20. Guo, L., Shi, X., He, D., Wang, Y., Ma, R., Qin, H., Wang, Y.: Practical learned lossless JPEG recompression with multi-level cross-channel entropy model in the DCT domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5862–5871 (2022)

  21. Yan, X., Di, Z., Huang, B., Li, M., Wang, W., Zeng, X., Fan, Y.: A high throughput and energy efficient Lepton hardware encoder with hash-based memory optimization. IEEE Trans. Circuits Syst. Video Technol. 32(7), 4680–95 (2021)

    Article  Google Scholar 

  22. Duda, J., Tahboub, K., Gadgil, N.J., Delp, E.J.: in The use of asymmetric numeral systems as an accurate replacement for Huffman coding. In: 2015 Picture Coding Symposium (PCS), pp. 65–69. IEEE (2015)

  23. Gupta, A., Bansal, A., Khanduja, V.: Modern lossless compression techniques: review, comparison and analysis. In: 2017 Second international conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–8. IEEE (2017)

  24. Wang, N., Wang, C., Lin, S.J.: A simplified variant of tabled asymmetric numeral systems with a smaller look-up table. Distrib Parallel Databases 39(3), 711 (2021)

    Article  Google Scholar 

  25. Xu, X., Akhtar, Z., Govindan, R., Lloyd, W., Ortega, A.: Context adaptive thresholding and entropy coding for very low complexity JPEG transcoding. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1392–1396. IEEE (2016)

  26. Kowarschik, M., Weiß, C.: An overview of cache optimization techniques and cache-aware numerical algorithms. In: Algorithms for Memory Hierarchies, pp. 213–232. Springer, Berlin, Heidelberg (2003)

    Chapter  MATH  Google Scholar 

  27. Kodak, E.: Kodak lossless true color image suite (photocd pcd0992). http://r0k.us/graphics/kodak/ (1993)

  28. Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 126–135 (2017)

  29. Workshop and challenge on learned image compression. https://www.compression.cc/challenge/

Download references

Author information

Authors and Affiliations

Authors

Contributions

QS: Conceptualization, Methodology. HZ: Software, Data curation, Writing-Original draft preparation. HS: Software Validation. CL: Software, Methodology Writing - Review & Editing. All authors reviewed the manuscript.

Corresponding author

Correspondence to Changcai Lai.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheng, Q., Zhu, H., Sheng, H. et al. High efficiency lossless image recompression algorithm with asymmetric numeral systems for real-time mobile application. J Real-Time Image Proc 20, 90 (2023). https://doi.org/10.1007/s11554-023-01346-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11554-023-01346-z

Keywords

Navigation