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Adaptive QP offset selection algorithm for virtual reality 360-degree video based on CTU complexity

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Abstract

Virtual reality 360-degree video requires ultra-high resolution to provide realistic feeling and dynamic perspective. Huge data volume brings new challenges to coding and transmission. Quantization parameter (QP) is one of the key parameters to control output bitrate and reconstruction quality during coding process. Many QP offset selection algorithms designed for this kind of video are based on latitude or Equirectangular Projection (ERP) weight maps, which cannot adapt to the situation of the flat block in tropical area or the complex block in polar area. In this paper, a new metric to measure complexity of Coding Tree Unit (CTU) is designed, and an adaptive QP offset selection algorithm is proposed based on CTU complexity to improve the quantization process. Each CTU is classified into one of the five categories according to its complexity, and then different QP offset value is determined for each category. By improving the quality of the visually sensitive area and reducing the bitrate of the flat one, the efficiency of the encoder is improved. The experimental results show that, compared with the HM16.20, the WS-PSNR increases by 0.40 dB, the BD-rate reduces by 1.99%, and the quality of visually sensitive areas has improved significantly.

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Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

References

  1. Abbas A, Adsumilli B (2016) AHG8: new GoPro test sequences for virtual reality video coding, Joint Video Exploration Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, 4th Meeting, document JVET-D0026, Chengdu, China

  2. Alshina E, Boyce J, Abbas A, Ye Y (editors) (2017) JVET common test conditions and evaluation procedures for 360° video, Joint Video Exploration Team of lTU-T SG16 WP3 and ISO/IEC JTC1/SC29/WG11 JVET-H1030, Macau

  3. Asbun E, He Y, He Y, Ye Y (2016) AHG8: InterDigital test sequences for virtual reality video coding, Joint Video Exploration Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, 4th Meeting, document JVET-D0039, Chengdu, China

  4. Bai HH, Zhu C, Zhao Y (2007) Optimized multiple description lattice vector quantization for wavelet image coding. IEEE Trans Circ Syst Video Tech 17:912–917

    Article  Google Scholar 

  5. Bai H, Lin W, Zhang M, Wang A, Zhao Y (2014) Multiple description video coding based on human visual system characteristics. IEEE Trans Circ Syst Vid Technol 24(8):1390–1394

    Article  Google Scholar 

  6. Chevallier O, Zhou N, Cercueil JP, He J, Loffroy R, Wang YXJ (2019) Comparison of tri-exponential decay versus bi-exponential decay and full fitting versus segmented fitting for modeling liver intravoxel incoherent motion diffusion MRI. NMR Biomed, Article Number e4155, https://doi.org/10.1002/nbm.4155

  7. Duanmu F, Kurdoglu E, Liu Y, Wang Y (2017) View Direction and Bandwidth Adaptive 360 Degree Video Streaming using a Two-Tier System, Proc. of IEEE International Symposium on Circuits and Systems, pp. 1-4, Baltimore, Maryland, USA

  8. He YS, Ni LM (2019) A novel scheme based on the diffusion to edge detection. IEEE Trans Image Process 28(4):1613–1624

    Article  MathSciNet  Google Scholar 

  9. He Y, Vishwanath B (2016) AHG8: InterDigital’s projection format conversion tool, Joint Video Exploration Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, 4th Meeting, document JVET-D0021, Chengdu, China

  10. Hoang ND (2019) Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression. Automat Construct 105

  11. Jarvinen A (2016) Virtual reality as trend Contextualising an emerging consumer Technology into trend analysis, Proceedings of 2016 Future Technologies Conference (FTC), pp. 1065-1070

  12. Khalil JE, Munteanu A, Lambert P (2019) Scalable wavelet-based coding of irregular meshes with interactive region-of-interest support. IEEE Trans Circ Syst Vid Technol 29(2):2067–2081

    Article  Google Scholar 

  13. Li Y, Xu J, Chen Z (2017) Spherical domain rate distortion optimization for 360 degree video coding [C]. IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, 709–714

  14. Papadopoulos MA, Zhang F, Agrafiotis D, Bull D (2016) AN ADAPTIVE QP OFFSET DETERMINATION METHOD FOR HEVC, IEEE International Conference on Image Processing ICIP, pp. 4220-4224

  15. Racape F, Galpin F, Rath G, Francois E (2017) AHG8: adaptive QP for 360 video coding, Joint Video Exploration Team of lTU-T SG16 WP3 and ISO/IEC JTC1/SC29/WG11 JVET-F0038, Hobart

  16. Schwarz S, Aminlou S (2016) Tampere pole vaulting sequence for virtual reality video coding, “Joint Video Exploration Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, 4th Meeting, document JVET-D0143, Chengdu, China

  17. Sun W, Guo R (2016) Test sequences for virtual reality video coding from letinvr, Joint Video Exploration Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, 4th Meeting, document JVET-D0179, Chengdu, China

  18. Sun Y, Yu L (2017) Coding optimization based on weighted-to-spherically-uniform quality metric for 360 video [C]. IEEE Vis Commun Image Process (VCIP):1–4

  19. Sun Y, Lu A, Yu L (2016) AHG8: WS-PSNR for 360 degree video objective quality evaluation, Joint Video Exploration Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, 4th Meeting, document JVET-D0040, Chengdu, China

  20. Tang CZ, Wang O, Liu F, Tan P (2019) Joint Stabilization and Direction of 360 degrees Videos, ACM Trans Graphics 38(2)

  21. Tran HTT, Ngoc NP, Bui CM, Pham MH , Thang TC (2017) An evaluation of quality metrics for 360 videos, 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), Milan, pp. 7-11

  22. Xiang GQ, Jia HZ, Yang MY, Li Y, Xie XD (2018) A novel adaptive quantization method for video coding. Multimed Tools Appl 77(12):14817–14840

    Article  Google Scholar 

  23. Xu YW, Yi SQ, Lin LQ, Chen WL, Zhao TS (2019) GOP structure-independent quantization parameter cascading in video coding. IEEE Access 7:76274–76282

    Article  Google Scholar 

  24. Zhang MM, Zhang J, Liu Z, An CZ (2019) An efficient coding algorithm for 360-degree video based on improved adaptive QP Compensation and early CU partition termination. Multimed Tools Appl 78(1):1081–1101

    Article  Google Scholar 

  25. Zhao T, Wang Z, Chen CW (2016) Adaptive quantization parameter cascading in HEVC hierarchical coding. IEEE Trans Image Process 25(7):2997–3009

    Article  MathSciNet  Google Scholar 

  26. Zhou Y, Tian L, Zhu C, Jin X, Sun Y (2020) Video coding optimization for virtual reality 360-degree source [J]. IEEE J Select Topics Signal Process 14(1):118–129

    Article  Google Scholar 

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Acknowledgements

This work is supported by the Beijing Municipal Natural Science Foundation (No.4202018), Great Wall Scholar Project of Beijing Municipal Education Commission (CIT&TCD20180304), and the National Natural Science Foundation of China (No.61972023).

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Authors and Affiliations

  1. School of Information Science and Technology, North China University of Technology, Beijing, 100041, China

    Zhi Liu, Ke Yang, Mengmeng Zhang & Fuqi Mao

  2. Yi Xinda (Beijing) Technology Co., Ltd, Beijing, 100080, China

    Xianya Fu

  3. Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China

    Zhao Wang

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  1. Zhi Liu
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  2. Ke Yang
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  3. Xianya Fu
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  4. Mengmeng Zhang
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  5. Zhao Wang
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Correspondence to Zhi Liu or Mengmeng Zhang.

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Liu, Z., Yang, K., Fu, X. et al. Adaptive QP offset selection algorithm for virtual reality 360-degree video based on CTU complexity. Multimed Tools Appl 80, 3951–3967 (2021). https://doi.org/10.1007/s11042-020-09922-2

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  • DOI: https://doi.org/10.1007/s11042-020-09922-2

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