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SSIM-Based Joint Bit-Allocation Using Frame Model Parameters for 3D Video Coding

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Computer Vision, Pattern Recognition, Image Processing, and Graphics (NCVPRIPG 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 841))

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Abstract

Optimum bit-allocation between texture video and depth map in 3D video results in better virtual view quality. To incorporate this, rate distortion optimization (RDO) property is used. The RDO in 3D video implies minimization of synthesis distortion at available rate. Several bit-allocation methods proposed in literature have not considered perceptual quality improvement. In this paper, we propose bit-allocation criteria that results in better visual quality of synthesized view. To achieve this, visual quality metrics are to be incorporated and structural similarity (SSIM) index is one of the metric that measures perceived quality. As SSIM gives similarity measure, we used dSSIM as distortion metric in mode decision and motion estimation instead of traditional metrics like mean square error (MSE) or sum of squared error (SSE). Synthesis distortion is modeled using dSSIM and joint bit-allocation is formulated as optimization problem that is solved using Lagrange multiplier method. Model parameters are determined at frame level for more accurate calculation of quantization parameters. BD-Rate evaluation shows a reduction in bit rate with improved SSIM.

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References

  1. Fehn, C.: A 3D-TV approach using depth-image-based rendering (DIBR). In: Proceedings of VIIP, vol. 3 (2003)

    Google Scholar 

  2. Fehn, C.: Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV. In: Electronic Imaging 2004, pp. 93–104. International Society for Optics and Photonics (2004)

    Google Scholar 

  3. Yuan, H., Chang, Y., Li, M., Yang, F.: Model based bit allocation between texture images and depth maps. In: International Conference On Computer and Communication Technologies in Agriculture Engineering (CCTAE), vol. 3, pp. 380–383. IEEE (2010)

    Google Scholar 

  4. Yuan, H.H., Chang, Y., Huo, J., Yang, F., Lu, Z.: Model-based joint bit allocation between texture videos and depth maps for 3-D video coding. IEEE Trans. Circ. Syst. Video Technol. 21(4), 485–497 (2011)

    Article  Google Scholar 

  5. Zhu, G., Jiang, G., Yu, M., Li, F., Shao, F., Peng, Z.: Joint video/depth bit allocation for 3D video coding based on distortion of synthesized view. In: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp. 1–6. IEEE (2012)

    Google Scholar 

  6. Shao, F., Jiang, G., Lin, W., Yu, M., Dai, Q.: Joint bit allocation and rate control for coding multi-view video plus depth based 3D video. IEEE Trans. Multimedia 15(8), 1843–1854 (2013)

    Article  Google Scholar 

  7. Yang, C., An, P., Shen, L.: Adaptive bit allocation for 3D video coding. Circ. Syst. Sig. Process. 36, 1–23 (2016)

    Google Scholar 

  8. Mai, Z.-Y., Yang, C.-L., Po, L.-M., Xie, S.-L.: A new rate-distortion optimization using structural information in H.264 I-frame encoder. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, pp. 435–441. Springer, Heidelberg (2005). https://doi.org/10.1007/11558484_55

    Chapter  Google Scholar 

  9. Huang, Y.-H., Ou, T.-S., Chen, H.H.: Perceptual-based coding mode decision. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), pp. 393–396 (2010)

    Google Scholar 

  10. Chen, Z., Lin, W., Ngan, K.N.: Perceptual video coding: challenges and approaches. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME), pp. 784–789 (2010)

    Google Scholar 

  11. Huang, Y.-H., Ou, T.-S., Su, P.-Y., Chen, H.H.: Perceptual rate-distortion optimization using structural similarity index as quality metric. IEEE Trans. Circ. Syst. Video Technol. 20(11), 1614–1624 (2010)

    Article  Google Scholar 

  12. Cui, Z., Gan, Z., Zhu, X.: Structural similarity optimal MB layer rate control for H. 264. In: Proceedings of IEEE International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–5 (2011)

    Google Scholar 

  13. Yeo, C., Tan, H.L., Tan, Y.H.: On rate distortion optimization using SSIM. IEEE Trans. Circ. Syst. Video Technol. 23(7), 1170–1181 (2013)

    Article  Google Scholar 

  14. Zhao, T., Wang, J., Wang, Z., Chen, C.W.: SSIM-based coarse-grain scalable video coding. IEEE Trans. Broadcast. 61(2), 210–221 (2015)

    Article  Google Scholar 

  15. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  16. Harshalatha, Y., Biswas, P.K.: Rate distortion optimization using SSIM for 3D video coding. In: 23rd International Conference on Pattern Recognition (ICPR), pp. 1261–1266. IEEE (2016)

    Google Scholar 

  17. Chen, H.H., Huang, Y.-H., Su, P.-Y., Ou, T.-S.: Improving video coding quality by perceptual rate-distortion optimization. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1287–1292 (2010)

    Google Scholar 

  18. 3DV-ATM Reference Software 3DV-ATMv5.lr2. http://mpeg3dv.nokiaresearch.com/svn/mpeg3dv/tags/3DV-ATMv5.1r2/. Accessed 06 Jan 2017

  19. View Synthesis Reference Software VSRS3.5. ftp://ftp.merl.com/pub/avetro/3dv-cfp/software/. Accessed 06 Jan 2017

  20. Fujii Laborotory, Nagoya University. http://www.fujii.nuee.nagoya-u.ac.jp/multiview -data/. Accessed 06 Jan 2017

  21. Zitnick, C.L., Kang, S.B., Uyttendaele, M., Winder, S., Szeliski, R.: High-quality video view interpolation using a layered representation. In: ACM Transactions on Graphics (TOG), vol. 23, no. 3, pp. 600–608. ACM (2004)

    Article  Google Scholar 

  22. Bjontegaard, G.: Calculation of Average PSNR Differences Between RD - curves. ITU-TQ.6/SG16 VCEG 13th Meeting. http://wftp3.itu.int/av-arch/video-site/0104_Aus/

  23. Harshalatha, Y., Biswas, P.K.: SSIM-based joint-bit allocation for 3D video coding. Multimedia Tools Appl. (2017, in Press). https://doi.org/10.1007/s11042-017-5327-0

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Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, India

    Y. Harshalatha & Prabir Kumar Biswas

Authors
  1. Y. Harshalatha
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  2. Prabir Kumar Biswas
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Correspondence to Y. Harshalatha .

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Editors and Affiliations

  1. Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Renu Rameshan

  2. Indraprastha Institute of Information Technology, New Delhi, India

    Chetan Arora

  3. Indian Institute of Technology, New Delhi, India

    Sumantra Dutta Roy

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Harshalatha, Y., Biswas, P.K. (2018). SSIM-Based Joint Bit-Allocation Using Frame Model Parameters for 3D Video Coding. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_5

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  • DOI: https://doi.org/10.1007/978-981-13-0020-2_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0019-6

  • Online ISBN: 978-981-13-0020-2

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