DeepAI
Log In Sign Up

A Bayesian Approach to Block Structure Inference in AV1-based Multi-rate Video Encoding

Due to differences in frame structure, existing multi-rate video encoding algorithms cannot be directly adapted to encoders utilizing special reference frames such as AV1 without introducing substantial rate-distortion loss. To tackle this problem, we propose a novel bayesian block structure inference model inspired by a modification to an HEVC-based algorithm. It estimates the posterior probabilistic distributions of block partitioning, and adapts early terminations in the RDO procedure accordingly. Experimental results show that the proposed method provides flexibility for controlling the tradeoff between speed and coding efficiency, and can achieve an average time saving of 36.1 (up to 50.6

READ FULL TEXT VIEW PDF

page 1

page 2

page 3

page 4

11/02/2019

A Generalized Rate-Distortion-λ Model Based HEVC Rate Control Algorithm

The High Efficiency Video Coding (HEVC/H.265) standard doubles the compr...
07/14/2018

Fast Block Structure Determination in AV1-based Multiple Resolutions Video Encoding

The widely used adaptive HTTP streaming requires an efficient algorithm ...
11/12/2020

CNN-based driving of block partitioning for intra slices encoding

This paper provides a technical overview of a deep-learning-based encode...
09/22/2020

H.264/SVC Mode Decision Based on Mode Correlation and Desired Mode List

The design of video encoders involves the implementation of fast mode de...
08/18/2020

PRNU Estimation from Encoded Videos Using Block-Based Weighting

Estimating the photo-response non-uniformity (PRNU) of an imaging sensor...
05/08/2022

SSIM-Variation-Based Complexity Optimization for Versatile Video Coding

To date, Versatile Video Coding (VVC) has a more magnificent overall per...
12/29/2020

Quality-Driven Dynamic VVC Frame Partitioning for Efficient Parallel Processing

VVC is the next generation video coding standard, offering coding capabi...

References

  • [1] Cisco. Cisco visual networking index: Forecast and methodology, 2016-2021. [Online]. Available: https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html
  • [2] O. Oyman and S. Singh, “Quality of experience for http adaptive streaming services,” IEEE Communications Magazine, vol. 50, no. 4, pp. 20–27, April 2012.
  • [3] Wikipedia. Aomedia video 1. [Online]. Available: https://en.wikipedia.org/wiki/AOMedia_Video_1
  • [4] AOM. Av1 codec library. [Online]. Available: https://aomedia.googlesource.com/aom/
  • [5] D. Schroeder, A. Ilangovan, M. Reisslein, and E. Steinbach, “Efficient multi-rate video encoding for hevc-based adaptive http streaming,” IEEE Transactions on Circuits and Systems for Video Technology, vol. PP, no. 99, pp. 1–1, 2017.
  • [6] J. D. Praeter, A. J. Díaz-Honrubia, N. V. Kets, G. V. Wallendael, J. D. Cock, P. Lambert, and R. V. de Walle, “Fast simultaneous video encoder for adaptive streaming,” in 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP), Oct 2015, pp. 1–6.
  • [7] D. Schroeder, P. Rehm, and E. Steinbach, “Block structure reuse for multi-rate high efficiency video coding,” in 2015 IEEE International Conference on Image Processing (ICIP), Sept 2015, pp. 3972–3976.
  • [8] Z. Zhao and P. Liang, “A highly efficient parallel algorithm for h.264 video encoder,” in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 5, May 2006, pp. V–V.
  • [9] ——, “Data partition for wavefront parallelization of h.264 video encoder,” in 2006 IEEE International Symposium on Circuits and Systems, May 2006, pp. 4 pp.–2672.
  • [10] VideoLAN. x264. [Online]. Available: https://www.videolan.org/developers/x264.html
  • [11] Z. Wen, B. Guo, J. Liu, J. Li, Y. Lu, and J. Wen, “Novel 3d-wpp algorithms for parallel hevc encoding,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 2016, pp. 1471–1475.
  • [12] T. Shen, Y. Lu, Z. Wen, L. Zou, Y. Chen, and J. Wen, “Ultra fast h.264/avc to hevc transcoder,” in 2013 Data Compression Conference, March 2013, pp. 241–250.
  • [13] Y. Chen, Z. Wen, J. Wen, M. Tang, and P. Tao, “Efficient software h.264/avc to hevc transcoding on distributed multicore processors,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 8, pp. 1423–1434, Aug 2015.
  • [14] I. Ahmad, X. Wei, Y. Sun, and Y.-Q. Zhang, “Video transcoding: an overview of various techniques and research issues,” IEEE Transactions on Multimedia, vol. 7, no. 5, pp. 793–804, Oct 2005.
  • [15] B. Li and J. Liu, “Multirate video multicast over the internet: an overview,” IEEE Network, vol. 17, no. 1, pp. 24–29, Jan 2003.
  • [16] D. H. Finstad, H. K. Stensland, H. Espeland, and P. Halvorsen, “Improved multi-rate video encoding,” in 2011 IEEE International Symposium on Multimedia, Dec 2011, pp. 293–300.
  • [17] Q. Hu, Z. Shi, X. Zhang, and Z. Gao, “Early skip mode decision based on bayesian model for hevc,” in 2015 Visual Communications and Image Processing (VCIP), Dec 2015, pp. 1–4.
  • [18] X. Shen, L. Yu, and J. Chen, “Fast coding unit size selection for hevc based on bayesian decision rule,” in 2012 Picture Coding Symposium, May 2012, pp. 453–456.
  • [19] C. Cai, S. Yin, X. Zhang, and Z. Gao, “An efficient hevc multi-rate encoding system based on x265,” in 2016 Visual Communications and Image Processing (VCIP), Nov 2016, pp. 1–4.
  • [20] AOM. v0.1.0, aom, git at google. [Online]. Available: https://aomedia.googlesource.com/aom/+/v0.1.0
  • [21] G.Bjontegaard, “Calculation of average psnr differences between rd curves,” in Doc. VCEG-M33 ITU-T Q6/16, 2001.
  • [22] ——, “Improvements of the bd-psnr model,” in ITU-T SG16 Q, vol. 6, 2008, p. 35.