Skip to main content
SpringerLink
Log in

A novel in-loop filtering mechanism of HEVC based on 3D sub-bands and CNN processing

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

Abstract

H.265/HEVC (high efficiency video coding) adopts in-loop filters to reduce artifacts such as blocking artifacts, ringing artifacts, but those above-mentioned artifacts are still obvious in the case of large quantization step. This paper presents a novel in-loop filtering mechanism based on three-dimensional (3D) sub-bands in adaptive group of frames and convolutional neural network (CNN) models for further improvement of in-loop filtering capability. Firstly, the video frame sequence is adaptively grouped; then, a group of video frames is decomposed into frequency sub-bands by 3D wavelet transform; afterward, frequency sub-bands with different types are filtered by corresponding CNN model; finally, the group of video frames is synthesized by 3D wavelet inverse transform. We apply the nonlinear mapping ability of CNN into the modification of wavelet coefficients. The sub-bands of high frequency and low frequency are filtered by four offline CNN models with different directional characteristics trained with training data generated by 3D wavelet transform, respectively. The details of the high frequency part are enhanced, while the quality of low-pass image is promoted, so that those artifacts can be effectively alleviated. By means of comparing experiments, the subjective and objective results show that our proposed method has better filtering performance than the in-loop filtering mechanism in HM16.18; especially the video frame quality can be effectively improved in the case of large quantization step.

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

Similar content being viewed by others

References

  1. Sullivan, G.J., Ohm, J.R., Han, W.J., Wiegand, T.: Overview of the high efficiency video coding (HEVC) standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1649–1668 (2013)

    Article  Google Scholar 

  2. Norkin, A., Bjontegaard, G., Fuldseth, A., Narroschke, M., Ikeda, M., Andersson, K., Zhou, M., Auwera, G.V.D.: HEVC deblocking filter. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1746–1754 (2013)

    Article  Google Scholar 

  3. Fu, C.M., Alshina, E., Alshin, A., Huang, Y.W., Chen, C.Y., Tsai, C.Y., Hsu, C.W., Lei, S.M., Park, J.H., Han, W.J.: Sample adaptive offset in the HEVC standard. IEEE Trans. Circuits Syst. Video Technol. 22(12), 1755–1764 (2013)

    Article  Google Scholar 

  4. Donoho, D.L.: Denoising via soft thresholding. IEEE Trans. Inf. Theory 41, 613–627 (1992)

    Article  Google Scholar 

  5. Pang, K.K., Tan, T.K.: Optimum loop filter in hybrid coders. IEEE Trans. Circuits Syst. Video Technol. 4(2), 158–167 (2002)

    Article  Google Scholar 

  6. Kim, S.D., Yi, J., Ra, J.B.: Deblocking filter with two separate modes in block-based video coding. In: Photonics West ‘98 Electronic Imaging, pp. 156–160 (1998)

  7. Watanabe, T., Wada, N., Yasuda, G., Tanizawa, A., Chujoh, T., Yamakage, T.: In-loop filter using block-based filter control for video coding. In: IEEE International Conference on Image Processing, pp. 1013–1016 (2010)

  8. Zhang, X., Lin, W., Wang, S., Ma, S.: Nonlocal adaptive in-loop filter via content-dependent soft-thresholding for HEVC. In: IEEE International Symposium on Multimedia, pp. 465–470 (2016)

  9. Dong, C., Chen, C.L., He, K., Tang, X.: Image Super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)

    Article  Google Scholar 

  10. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)

  11. Park, W.S., Kim, M.: CNN-based in-loop filtering for coding efficiency improvement. In: Image, Video, and Multidimensional Signal Processing Workshop, pp. 1–5 (2016)

  12. Zhang, Y., Shen, T., Ji, X., Zhang, Y., Xiong, R., Dai, Q.: Residual highway convolutional neural networks for in-loop filtering in HEVC. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 27(8), 3827 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  13. Dai, Y., Liu, D., Wu, F.: A convolutional neural network approach for post-processing in hevc intra coding. In: International Conference on Multimedia Modeling, pp. 28–39 (2017)

  14. Pearlman, W.A., Kim, B.J., Xiong, Z.: Embedded video subband coding with 3D SPIHT. Wavelet Image Video Compress. 450, 397–432 (2002)

    Article  Google Scholar 

  15. Qing, L., Zeng, W.: Context-adaptive modeling for wavelet-domain distributed video coding. IEEE Multimedia 21(4), 84–93 (2014)

    Article  Google Scholar 

  16. Shapiro, J.M.: Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Process. 41(12), 3445–3462 (2002)

    Article  MATH  Google Scholar 

  17. Christopoulos, C., Skodras, A., Ebrahimi, T.: The JPEG2000 still image coding system: an overview. IEEE Trans. Consum. Electron. 46(4), 1103–1127 (2000)

    Article  Google Scholar 

  18. Chen, G., Zhu, W.P., Xie, W.: Wavelet-based image denoising using three scales of dependency. IET Image Process. 6(6), 756–760 (2012)

    Article  MathSciNet  Google Scholar 

  19. Peng, Y.H.: An improved thresholding method in wavelet transform domain for denosing. J. China Inst. Commun. 24, 114–124 (2004)

    Google Scholar 

  20. Om, H., Biswas, M.: An improved image denoising method based on wavelet thresholding. J. Signal Inf. Process. 3(1), 109–116 (2012)

    Google Scholar 

  21. Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. Publ. Am. Stat. Assoc. 90(432), 1200–1224 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  22. Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 9(9), 1532 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  23. Itut, R.P.: Subjective video quality assessment methods for multimedia applications. Recomm. ITU-T P.910 12(2), 3665–3673 (1999)

    Google Scholar 

  24. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989). https://doi.org/10.1109/34.192463

    Article  MATH  Google Scholar 

  25. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: ACM International Conference on Multimedia, pp. 675–678 (2014)

  26. HM version 16.18. https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware/tags/HM-16.18/

  27. Bossen, F.: Common test conditions and software reference configurations. In: Joint Collaborative Team on Video Coding (2011)

  28. Bjontegaard, G.: Calculation of average PSNR differences between RD curves. In: VCEG Meeting, Austin, Texas, USA (2001)

  29. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Proceedings of the IEEE Asilomar Conference on Signals, Systems and Computers, vol. 1392, pp. 1398–1402 (2003)

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61671141, 61401081), the Liaoning Provincial Natural Science Foundation of China (No. 20180551007), and the Ministry of Education-China Mobile Scientific Research Funds (No. MCM20150103).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Dacheng Zhang, Weimin Lei, Wei Zhang & Xinyi Chen

Authors
  1. Dacheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weimin Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weimin Lei.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, D., Lei, W., Zhang, W. et al. A novel in-loop filtering mechanism of HEVC based on 3D sub-bands and CNN processing. SIViP 13, 1045–1053 (2019). https://doi.org/10.1007/s11760-019-01444-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01444-1

Keywords

Search

Navigation