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Video complexity analyzer (VCA) for streaming
applications
December 14, 2021
Presenters: Vignesh V Menon & Hadi Amirpour
1
All rights reserved. ©2020
Vignesh V Menon Hadi Amirpour
Researchers @ Christian Doppler Laboratory ATHENA, University of Klagenfurt, Austria
All rights reserved. ©2021
2
About Us
All rights reserved. ©2020
● Motivation for VCA
● Features
● Experimental Results
● Applications
● Future Roadmap
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3
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Motivation
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Motivation
● We aim to develop online prediction systems tailor-made for live streaming applications.
● The state-of-the-art spatial and temporal complexity feature is SI-TI.
Fig.1: Correlation of SI feature with number of bits (in kb) per frame in IDR encoding with QP27 of x265 for 24 test sequences from MCML[1] and
SJTU[2] dataset.
[1] M. Cheon and J.-S. Lee, “Subjective and Objective Quality Assessment of Compressed 4K UHD Videos for Immersive Experience,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 28,
no. 7, pp. 1467–1480, 2018.
[2] L. Song, X. Tang, W. Zhang, X. Yang, and P. Xia, “The SJTU 4K Video Sequence Dataset,”Fifth International Workshop on Quality of Multimedia Experience (QoMEX2013), Jul. 2013.
Pearson correlation coefficient (PCC) of SI with bits per frame is ~0.79.
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Motivation
● Time taken to compute SI-TI features is very high!
➢ ~0.05 seconds per frame for 1080p, ~0.2 seconds per frame for 2160p
➢ Not suitable for live applications
➢ Higher computational cost in VoD applications
Fig.2: Time taken to compute SI-TI features [1] in Intel Xeon Gold 5218R
[1] SITI source code: https://github.com/Telecommunication-Telemedia-Assessment/SITI
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Motivation
Video Complexity Analyzer (VCA) can be realized as a fast preprocessor which
determines the spatial and temporal complexity of videos (segments) to aid the encoding
process.
Fig.3: The proposed framework for streaming applications.
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Features
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Spatial complexity feature
k is the block address in the pth frame,w×w pixels is the size of the block, and DCT(i, j) is
the (i, j)th
DCT component when i+j >1, and 0 otherwise.
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Spatial complexity feature
C represents the number of blocks per frame.
Fig. 4: Number of bits (in kb) per frame and E feature of Wood SJTU sequence.
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Temporal complexity feature
The block-wise SAD of the texture energy of each frame (p) compared to its previous
frame (p-1) is computed.
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Experimental Results
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Results
PCC(SI, Bits per frame) = 0.787
PCC(E, Bits per frame) = 0.856
Fig. 5: Correlation of SI and E features with number of bits (in kb) per frame.
14
Results
Fig. 6: Average time to compute E-h for various resolutions (with x86 SIMD).
Note: Presently, E-h computation is 5 times faster than SI-TI computation.
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Applications
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Shot Detection
We define the gradient of ‘h’ per frame ‘p’ as:
Fig. 7: Epsilon values for ToS sequence. Please note that shot transitions happen at frames: 107, 110,
238,338,465,531,596,778,850,917,1018,1361,1437.
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Shot Detection
The algorithm is classified into two steps:
● Feature extraction
● Successive Elimination Algorithm
Source: V. V. Menon, H. Amirpour, M. Ghanbari, and C. Timmerer,“Efficient Content-Adaptive Feature-Based Shot Detection for HTTP Adaptive Streaming,”
in 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 2174–2178.
Benchmark is the default shot detection algorithm of x265.
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Future Roadmap
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Future Roadmap
● The initial version will be released before March 1, 2022.
● Adding Multi-threading support
○ H computation for blocks in each frame can be realized concurrently.
○ ~6x speedup expected with 8 threads.
● Adding CUDA/ OpenCL support
All rights reserved. ©2020
Thanks for your attention!
All rights reserved. ©2021
20
Vignesh V Menon (vignesh.menon@aau.at)
Hadi Amirpour (hadi.amirpour@aau.at)

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Video complexity analyzer (VCA) for streaming applications

  • 1. All rights reserved. ©2020 All rights reserved. ©2021 Video complexity analyzer (VCA) for streaming applications December 14, 2021 Presenters: Vignesh V Menon & Hadi Amirpour 1
  • 2. All rights reserved. ©2020 Vignesh V Menon Hadi Amirpour Researchers @ Christian Doppler Laboratory ATHENA, University of Klagenfurt, Austria All rights reserved. ©2021 2 About Us
  • 3. All rights reserved. ©2020 ● Motivation for VCA ● Features ● Experimental Results ● Applications ● Future Roadmap All rights reserved. ©2021 3
  • 4. All rights reserved. ©2020 Motivation All rights reserved. ©2021 4
  • 5. 5 Motivation ● We aim to develop online prediction systems tailor-made for live streaming applications. ● The state-of-the-art spatial and temporal complexity feature is SI-TI. Fig.1: Correlation of SI feature with number of bits (in kb) per frame in IDR encoding with QP27 of x265 for 24 test sequences from MCML[1] and SJTU[2] dataset. [1] M. Cheon and J.-S. Lee, “Subjective and Objective Quality Assessment of Compressed 4K UHD Videos for Immersive Experience,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 7, pp. 1467–1480, 2018. [2] L. Song, X. Tang, W. Zhang, X. Yang, and P. Xia, “The SJTU 4K Video Sequence Dataset,”Fifth International Workshop on Quality of Multimedia Experience (QoMEX2013), Jul. 2013. Pearson correlation coefficient (PCC) of SI with bits per frame is ~0.79.
  • 6. 6 Motivation ● Time taken to compute SI-TI features is very high! ➢ ~0.05 seconds per frame for 1080p, ~0.2 seconds per frame for 2160p ➢ Not suitable for live applications ➢ Higher computational cost in VoD applications Fig.2: Time taken to compute SI-TI features [1] in Intel Xeon Gold 5218R [1] SITI source code: https://github.com/Telecommunication-Telemedia-Assessment/SITI
  • 7. 7 Motivation Video Complexity Analyzer (VCA) can be realized as a fast preprocessor which determines the spatial and temporal complexity of videos (segments) to aid the encoding process. Fig.3: The proposed framework for streaming applications.
  • 8. All rights reserved. ©2020 Features All rights reserved. ©2021 8
  • 9. 9 Spatial complexity feature k is the block address in the pth frame,w×w pixels is the size of the block, and DCT(i, j) is the (i, j)th DCT component when i+j >1, and 0 otherwise.
  • 10. 10 Spatial complexity feature C represents the number of blocks per frame. Fig. 4: Number of bits (in kb) per frame and E feature of Wood SJTU sequence.
  • 11. 11 Temporal complexity feature The block-wise SAD of the texture energy of each frame (p) compared to its previous frame (p-1) is computed.
  • 12. All rights reserved. ©2020 Experimental Results All rights reserved. ©2021 12
  • 13. 13 Results PCC(SI, Bits per frame) = 0.787 PCC(E, Bits per frame) = 0.856 Fig. 5: Correlation of SI and E features with number of bits (in kb) per frame.
  • 14. 14 Results Fig. 6: Average time to compute E-h for various resolutions (with x86 SIMD). Note: Presently, E-h computation is 5 times faster than SI-TI computation.
  • 15. All rights reserved. ©2020 Applications All rights reserved. ©2021 15
  • 16. 16 Shot Detection We define the gradient of ‘h’ per frame ‘p’ as: Fig. 7: Epsilon values for ToS sequence. Please note that shot transitions happen at frames: 107, 110, 238,338,465,531,596,778,850,917,1018,1361,1437.
  • 17. 17 Shot Detection The algorithm is classified into two steps: ● Feature extraction ● Successive Elimination Algorithm Source: V. V. Menon, H. Amirpour, M. Ghanbari, and C. Timmerer,“Efficient Content-Adaptive Feature-Based Shot Detection for HTTP Adaptive Streaming,” in 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 2174–2178. Benchmark is the default shot detection algorithm of x265.
  • 18. All rights reserved. ©2020 Future Roadmap All rights reserved. ©2021 18
  • 19. 19 Future Roadmap ● The initial version will be released before March 1, 2022. ● Adding Multi-threading support ○ H computation for blocks in each frame can be realized concurrently. ○ ~6x speedup expected with 8 threads. ● Adding CUDA/ OpenCL support
  • 20. All rights reserved. ©2020 Thanks for your attention! All rights reserved. ©2021 20 Vignesh V Menon (vignesh.menon@aau.at) Hadi Amirpour (hadi.amirpour@aau.at)