IP Library › Granted Patent US 12,368,864
Granted Patent B2
US 12,368,864 · App. 18/364,821 · Granted Jul 22, 2025

Facilitating encoding of video data using neural network

Inventors: Sam Tak Wu Kwong (Kowloon, HK); Yunhao Mao (Kowloon, HK); Shiqi Wang (Kowloon, HK)
Assignee: City University of Hong Kong
H04N19/149H04N19/159H04N19/1883
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,368,864
App. No.
18/364,821
Granted
Jul 22, 2025
Kind
B2
Abstract

A computer-implemented method for facilitating encoding of video data. The method includes performing an operation to determine prediction residuals associated with a unit of the video data, and, processing, using a neural network arrangement, the prediction residuals associated with the unit of the video data to determine model parameters associated with a rate-distortion model for the unit of the video data. The model parameters are arranged to facilitate encoding of at least the unit of the video data. The method can be applied to multiple ones of such unit of the video data.

Claims (124)

1. A computer-implemented method for facilitating encoding of video data, comprising:

performing an operation to determine prediction residuals associated with a unit of the video data; and

processing, using a neural network arrangement, the prediction residuals associated with the unit of the video data to determine model parameters associated with a rate-distortion model for the unit of the video data;

wherein the model parameters are arranged to facilitate encoding of at least the unit of the video data; the model parameters comprising a bit-rate related model parameter α and a distortion related model parameter β;

wherein the neural network arrangement comprises:

a first neural network arrangement arranged to process the prediction residuals associated with the unit of the video data to determine the model parameters for intra-frame coding;

and/or

a second neural network arrangement arranged to process the prediction residuals associated with the unit of the video data to determine the model parameters for inter-frame coding, wherein the second neural network arrangement comprises:

a first neural network arranged to process the prediction residuals associated with the unit of the video data to determine the bit-rate related model parameter α for inter-frame coding; and

a second neural network arranged to process the prediction residuals associated with the unit of the video data to determine the distortion related model parameter β for inter-frame coding.

2. The computer-implemented method of claim 1 , wherein the unit of the video data corresponds to a coding tree unit of the video data.

3. The computer-implemented method of claim 2 , wherein the operation comprises partitioning the unit of the video data using at least one of the following partition schemes: quad-tree (QT), binary-tree (BT), or ternary-tree (TT).

4. The computer-implemented method of claim 3 , wherein the operation comprises partitioning the unit of the video data using a quad-tree (QT) partition scheme.

5. The computer-implemented method of claim 2 ,

wherein the operation comprises processing, using an encoder, part of the video data containing the unit of the video data;

wherein the part of the video data correspond to video data of a frame of a video.

6. The computer-implemented method of claim 5 , wherein the encoder is a Versatile Video Coding (VVC) based encoder.

7. The computer-implemented method of claim 1 , wherein the first neural network arrangement comprises:

a first neural network arranged to process the prediction residuals associated with the unit of the video data to determine the bit-rate related model parameter α for intra-frame coding; and

a second neural network arranged to process the prediction residuals associated with the unit of the video data to determine the distortion related model parameter β for intra-frame coding.

8. The computer-implemented method of claim 7 ,

wherein the first neural network of the first neural network arrangement comprises a feature extractor and a regressor; and/or

wherein the second neural network of the first neural network arrangement comprises a feature extractor and a regressor.

9. The computer-implemented method of claim 7 , wherein the first neural network and the second neural network of the first neural network arrangement have substantially the same neural network structure.

10. The computer-implemented method of claim 1 ,

wherein the first neural network of the second neural network arrangement comprises a feature extractor and a regressor; and/or

wherein the second neural network of the second neural network arrangement comprises a feature extractor and a regressor.

11. The computer-implemented method of claim 1 , wherein the first neural network and the second neural network of the second neural network arrangement have substantially the same neural network structure.

12. A computer-implemented method for facilitating encoding of video data, comprising:

performing an operation to determine prediction residuals associated with a unit of the video data; and

processing, using a neural network arrangement, the prediction residuals associated with the unit of the video data to determine model parameters associated with a rate-distortion model for the unit of the video data;

wherein the model parameters are arranged to facilitate encoding of at least the unit of the video data; the model parameters comprising a bit-rate related model parameter α and a distortion related model parameter β;

wherein the rate-distortion model is representable as:

D

=

α

⁢

β

R

+

C

D

⁢

Q

where R denotes bit-rate, D denotes distortion, and C DQ is a constant.

13. A computer-implemented method for facilitating encoding of video data, comprising:

performing an operation to determine prediction residuals associated with a unit of the video data; and

processing, using a neural network arrangement, the prediction residuals associated with the unit of the video data to determine model parameters associated with a rate-distortion model for the unit of the video data; and

adjusting the determined model parameters using one or more adjustment factors to obtain adjusted model parameters to facilitate encoding of at least the unit of the video data;

wherein the model parameters are arranged to facilitate encoding of at least the unit of the video data; the model parameters comprising a bit-rate related model parameter α and a distortion related model parameter β;

wherein the neural network arrangement comprises:

a first neural network arrangement arranged to process the prediction residuals associated with the unit of the video data to determine the model parameters for intra-frame coding;

and/or

a second neural network arrangement arranged to process the prediction residuals associated with the unit of the video data to determine the model parameters for inter-frame coding; and

wherein the adjusting of the determined model parameters comprises:

adjusting the bit-rate related model parameter α using a first adjustment factor to obtain adjusted bit-rate related model parameter β; and/or

adjusting the distortion related model parameter β using a second adjustment factor to obtain adjusted distortion related model parameter β.

14. The computer-implemented method of claim 13 , wherein the adjusting of the bit-rate related model parameter α comprises:

adjusting the bit-rate related model parameter α for intra-frame coding using the first adjustment factor to obtain adjusted bit-rate related model parameter α i for inter-frame coding; or

adjusting the bit-rate related model parameter α for intra-frame coding using the first adjustment factor to obtain adjusted bit-rate related model parameter α i for intra-frame coding.

15. The computer-implemented method of claim 13 , wherein the adjusting of the distortion related model parameter β comprises:

adjusting the distortion related model parameter β for intra-frame coding using the second adjustment factor to obtain adjusted distortion related model parameter β i for inter-frame coding; or

adjusting the distortion related model parameter β for intra-frame coding using the second adjustment factor to obtain adjusted distortion related model parameter β i for intra-frame coding.

16. The computer-implemented method of claim 13 , further comprising:

determining a target bit-rate for the unit of the video data based on the adjusted model parameters;

determining a quantization step size for the unit of the video data based on the target bit-rate;

determining coding parameters for encoding the unit of the video data based on the quantization step size; and

encoding the unit of the video data based on the coding parameters.

17. The computer-implemented method of claim 16 , wherein the determining of the quantization step size for the unit of the video data is based on:

Q

i

=

α

i

R

i

where Q i is the quantization step size for the unit of the video data and R i is the target bit-rate for the unit of the video data.

18. The computer-implemented method of claim 17 , wherein the coding parameters for encoding the unit of the video data comprises a quantization parameter QD i and a Lagrangian parameter λ i .

19. The computer-implemented method of claim 18 , wherein the determining of the coding parameters for encoding the unit of the video data is based on:

Q

⁢

P

i

=

(

log

X

1

⁢

Q

i

)

×

X

2

+

X

3

⁢

and

⁢

λ

i

=

Y

1

×

Y

2

QP

i

Y

3

where X 1 , X 2 , X 3 , Y 1 , Y 2 , Y 3 are real numbers.

20. The computer-implemented method of claim 16 , wherein the encoding of the unit of the video data is performed using Versatile Video Coding (VVC) based encoding technique.

21. The computer-implemented method of claim 16 , further comprising, after encoding of the unit of the video data:

updating the first adjustment factor to obtain an updated first adjustment factor for use in subsequent bit-rate related model parameter adjustment; and/or

updating the second adjustment factor to obtain an updated second adjustment factor for use in subsequent distortion related model parameter adjustment.

22. The computer-implemented method of claim 21 , wherein the updating of the first adjustment factor is based on: an actual output bit-rate and an actual quantization step size associated with the encoding of the unit of the video data, and the bit-rate related model parameter α for the unit of the video data.

23. The computer-implemented method of claim 21 , wherein the updating of the second adjustment factor is based on: an output bit-rate and a Lagrangian parameter associated with the encoding of the unit of the video data, the updated first adjustment factor, and the bit-rate related model parameter α and the distortion related model parameter β for the unit of the video data.

24. A system for facilitating encoding of video data, comprising:

one or more processors; and

memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for performing the computer-implemented method of claim 1 .

25. A non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors, the one or more programs including instructions for performing the computer-implemented method of claim 1 .

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 3, 2023
From: KWONG, SAM TAK WU; MAO, YUNHAO; WANG, SHIQI
To: CITY UNIVERSITY OF HONG KONG
Reel/Frame 064490/0445 →
Continuity (1)
Related Publication 20250047864A1 · Feb 6, 2025
References Cited (85)
US 6831947B2 · Ribas Corbera · 2004 [cited by applicant]
US 8532169B2 · Wang et al. · 2013 [cited by applicant]
US 8588296B2 · Yang et al. · 2013 [cited by applicant]
US 8897370B1 · Wang · 2014 [cited by examiner]
US 9860543B2 · Xu et al. · 2018 [cited by applicant]
US 10542262B2 · Gao et al. · 2020 [cited by applicant]
US 10560696B2 · Gao et al. · 2020 [cited by applicant]
US 11025914B1 · Yuen et al. · 2021 [cited by applicant]
US 20110310962A1 · Ou · 2011 [cited by examiner]
US 20220256169A1 · Siddaramanna · 2022 [cited by examiner]
CN 106416251B · 2020 [cited by applicant]
CN 107113432B · 2020 [cited by applicant]
JP 6019189B2 · 2016 [cited by applicant]
Z. Liu, Z. Chen, Y. Li, Y. Wu, and S. Liu, “AHG10: Quality dependency factor based rate control for VVC,” JVET M0600, Jan. 2019. [cited by applicant]
G. Ren, J. Jia, J. Wang, Z. Chen, and Z. Liu, “AHG10: An improved VVC rate control scheme,” JVET Y0105, Jan. 2022. [cited by applicant]
B. Xu, X. Pan, Y. Zhou, Y. Li, D. Yang, and Z. Chen, “CNN-based rate-distortion modeling for H. 265/HEVC,” in 2017 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2017, pp. 1-4. [cited by applicant]
M. Santamaria, E. Izquierdo, S. Blasi, and M. Mrak, “Estimation of rate control parameters for video coding using CNN,” in 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2018, pp. 1-4. [cited by applicant]
Y. Li, B. Li, D. Liu, and Z. Chen, “A convolutional neural network-based approach to rate control in HEVC intra coding,” in 2017 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2017, pp. 1-4. [cited by applicant]
M. Zhou, X. Wei, S. Kwong, W. Jia, and B. Fang, “Rate control method based on deep reinforcement learning for dynamic video sequences in HEVC,” IEEE Transactions on Multimedia, vol. 23, pp. 1106-1121, 2020. [cited by applicant]
X. Zhao, S.-H. Kim, Y. Zhao, H. E. Egilmez, M. Koo, S. Liu, J. Lainema, and M. Karczewicz, “Transform coding in the VVC standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, No. 10, pp. 387… [cited by applicant]
C.-Y. Tsai, C.-Y. Chen, T. Yamakage, I. S. Chong, Y.-W. Huang,C.-M. Fu, T. Itoh, T. Watanabe, T. Chujoh, M. Karczewicz, and S.-M. Lei, “Adaptive loop filtering for video coding,” IEEE Journal of Selected Topics in Signa… [cited by applicant]
K. Andersson, K. Misra, M. Ikeda, D. Rusanovskyy, and S. Iwamura, “Deblocking filtering in VVC,” in 2021 Picture Coding Symposium (PCS), 2021, pp. 1-5. [cited by applicant]
Z. Ni, W. Yang, S. Wang, L. Ma, and S. Kwong, “Towards unsupervised deep image enhancement with generative adversarial network,” IEEE Transactions on Image Processing, vol. 29, pp. 9140-9151, 2020. [cited by applicant]
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning. PMLR, 2015,pp. 448-456. [cited by applicant]
A. L. Maas, A. Y. Hannun, A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. icml, vol. 30, No. 1. Citeseer, 2013, p. 3. [cited by applicant]
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, No. 11, pp. 2278-2324, 1998. [cited by applicant]
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings… [cited by applicant]
D. Ma, F. Zhang, and D. R. Bull, “BVI-DVC: a training database for deep video compression,” arXiv preprint arXiv:2003.13552, 2020. [cited by applicant]
F. Bossen, J. Boyce, K. Suehring, X. Li, and V. Seregin, “VTM common test conditions and software reference configurations for SDR video,” JVET T2010, Oct. 2020. [cited by applicant]
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural informatio… [cited by applicant]
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international con-ference on computer vision, 2015, pp. 1026… [cited by applicant]
D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. [cited by applicant]
J. Garrett-Glaser, “A novel macroblock-tree algorithm for high-performance optimization of dependent video coding in H. 264/AVC,” Tech. Rep., 2009. [cited by applicant]
J. Han, P. Wilkins, Y. Xu, and J. Bankoski, “A temporal dependency model for rate-distortion optimization in video coding,” in 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019, pp. 1-4. [cited by applicant]
Y. Mao, M. Wang, Z. Ni, S. Wang and S. Kwong, “Neural Network Based Rate Control for Versatile Video Coding,” in IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2023.3262303. [cited by applicant]
T. Sikora, “The MPEG-4 video standard verification model,” IEEE Transactions on circuits and systems for video technology, vol. 7, No. 1, pp. 19-31, 1997. [cited by applicant]
T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, “Overview of the H.264/AVC video coding standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, No. 7, pp. 560-576, 2003. [cited by applicant]
G. J. Sullivan, J. Ohm, W. Han, and T. Wiegand, “Overview of the high efficiency video coding (HEVC) standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, No. 12, pp. 1649-1668, 2012. [cited by applicant]
B. Bross, Y.-K. Wang, Y. Ye, S. Liu, J. Chen, G. J. Sullivan, and J.-R. Ohm, “Overview of the versatile video coding (VVC) standard and its applications,” IEEE Transactions on Circuits and Systems for Video Technology, … [cited by applicant]
Y.-W. Huang, J. An, H. Huang, X. Li, S.-T. Hsiang, K. Zhang, H. Gao, J. Ma, and O. Chubach, “Block partitioning structure in the VVC standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, No… [cited by applicant]
L. Zhang, K. Zhang, H. Liu, H. C. Chuang, Y. Wang, J. Xu, P. Zhao, and D. Hong, “History-based motion vector prediction in versatile video coding,” in 2019 Data Compression Confer-ence (DCC), 2019, pp. 43-52. [cited by applicant]
S. De-Luxán-Hernández, V. George, J. Ma, T. Nguyen, H. Schwarz, D. Marpe, and T. Wiegand, “An intra subpartition coding modefor VVC,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 1203-1207. [cited by applicant]
K. Zhang, Y.-W. Chen, L. Zhang, W.-J. Chien, and M. Karczewicz, “An improved framework of affine motion compensation in video coding,” IEEE Transactions on Image Processing, vol. 28, No. 3, pp. 1456-1469, 2019. [cited by applicant]
H. Liu, L. Zhang, K. Zhang, J. Xu, Y. Wang, J. Luo, and Y. He, “Adaptive motion vector resolution for affine-inter mode coding,” in 2019 Picture Coding Symposium (PCS), 2019, pp. 1-4. [cited by applicant]
L. Zhao, X. Zhao, S. Liu, X. Li, J. Lainema, G. Rath, F. Urban, and F. Racapé, “Wide angular intra prediction for versatile video coding,” in 2019 Data Compression Conference (DCC), 2019, pp. 53-62. [cited by applicant]
X. Zhao, J. Chen, M. Karczewicz, A. Said, and V. Seregin, “Joint separable and non-separable transforms for next-generation video coding,” IEEE Transactions on Image Pro-cessing, vol. 27, No. 5, pp. 2514-2525, 2018. [cited by applicant]
H. Schwarz, T. Nguyen, D. Marpe, and T. Wiegand, “Hybrid video coding with trellis-coded quantization,” in 2019 Data Compression Conference (DCC), Mar. 2019, pp. 182-191. [cited by applicant]
A. J. Viterbi, “Error bounds for convolutional codes and an asymptotically optimum decoding algorithm,” IEEE transactions on Information Theory, vol. 13, No. 2, pp. 260-269, 1967. [cited by applicant]
K. Andersson, J. Enhorn, R. Sjöberg, J. Ström, and L. Litwic, “Addition of a GOP hierarchy of 32 for random access config-uration for VTM,” JVET-S0180, Jun. 2020. [cited by applicant]
H. Yang, L. Shen, X. Dong, Q. Ding, P. An, and G. Jiang, “Low-complexity ctu partition structure decision and fast intra mode decision for versatile video coding,” IEEE Transactions on Circuits and Systems for Video Tec… [cited by applicant]
X. Dong, L. Shen, M. Yu, and H. Yang, “Fast intra mode decision algorithm for versatile video coding,” IEEE Transactions on Multimedia, vol. 24, pp. 400-414, 2022. [cited by applicant]
Z. Pan, S. Kwong, M.-T. Sun, and J. Lei, “Early MERGE mode decision based on motion estimation and hierarchical depth cor-relation for hevc,” IEEE Transactions on Broadcasting, vol. 60, No. 2, pp. 405-412, 2014. [cited by applicant]
S. Ma, X. Zhang, C. Jia, Z. Zhao, S. Wang, and S. Wang, “Image and video compression with neural networks: A review,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, No. 6, pp. 1683-1698, 2019. [cited by applicant]
X. He, Q. Hu, X. Han, X. Zhang, C. Zhang, and W. Lin, “Enhancing HEVC compressed videos with a partition-masked convolutional neural network,” in 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE,… [cited by applicant]
Z. Pan, X. Yi, Y. Zhang, B. Jeon, and S. Kwong, “Efficient in-loop filtering based on enhanced deep convolutional neural networks for HEVC,” IEEE Transactions on Image Processing, vol. 29, pp. 5352-5366, 2020. [cited by applicant]
Y. Wang, H. Zhu, Y. Li, Z. Chen, and S. Liu, “Dense residual convolutional neural network based in-loop filter for HEVC,” in 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2018, pp. 1-4. [cited by applicant]
N. Yan, D. Liu, H. Li, B. Li, L. Li, and F. Wu, “Convolutional neural network-based fractional-pixel motion compensation,” IEEE Transactions on Circuits and Systems for Video Technol-ogy, vol. 29, No. 3, pp. 840-853, 20… [cited by applicant]
J. Mao and L. Yu, “Convolutional neural network based bi-prediction utilizing spatial and temporal information in video coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, No. 7, pp. 1856-1… [cited by applicant]
Y. Wang, X. Fan, S. Liu, D. Zhao, and W. Gao, “Multi-scale convolutional neural network-based intra prediction for video coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, No. 7, pp. 1803-… [cited by applicant]
H. Sun, Z. Cheng, M. Takeuchi, and J. Katto, “Enhanced intra prediction for video coding by using multiple neural networks,” IEEE Transactions on Multimedia, vol. 22, No. 11, pp. 2764-2779, 2020. [cited by applicant]
L. Zhu, Y. Zhang, S. Wang, S. Kwong, X. Jin, and Y. Qiao, “Deep learning-based chroma prediction for intra versatile video coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, No. 8, pp. 316… [cited by applicant]
L. Zhu, S. Kwong, Y. Zhang, S. Wang, and X. Wang, “Generative adversarial network-based intra prediction for video coding,” IEEE transactions on multimedia, vol. 22, No. 1, pp. 45-58, 2019. [cited by applicant]
L. Zhao, S. Wang, X. Zhang, S. Wang, S. Ma, and W. Gao, “Enhanced motion-compensated video coding with deep virtual reference frame generation,” IEEE Transactions on Image Pro-cessing, vol. 28, No. 10, pp. 4832-4844, 20… [cited by applicant]
Z. Zhao, S. Wang, S. Wang, X. Zhang, S. Ma, and J. Yang, “Enhanced bi-prediction with convolutional neural network for high-efficiency video coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. … [cited by applicant]
C. Ma, D. Liu, X. Peng, L. Li, and F. Wu, “Convolutional neural network-based arithmetic coding for HEVC intra-predicted residues,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, No. 7, pp. 190… [cited by applicant]
R. Song, D. Liu, H. Li, and F. Wu, “Neural network-based arithmetic coding of intra prediction modes in HEVC,” in 2017 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2017, pp. 1-4. [cited by applicant]
H. Gish and J. N. Pierce, “Asymptotically efficient quantizing,” IEEE Transactions on Information Theory, vol. 14, No. 5, pp. 676-683, 1968. [cited by applicant]
T. Eude, R. Grisel, H. Cherifi, and R. Debrie, “On the distribution of the DCT coefficients,” in Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing, vol. v, 1994, pp. V/3… [cited by applicant]
E. Y. Lam and J. W. Goodman, “A mathematical analysis of the DCT coefficient distributions for images,” IEEE Transactions on Image Processing, vol. 9, No. 10, pp. 1661-1666, 2000. [cited by applicant]
E. Yang, X. Yu, J. Meng, and C. Sun, “Transparent composite model for DCT Coefficients: Design and Analysis,” IEEE Transactions on Image Processing, vol. 23, No. 3, pp. 1303-1316, 2014. [cited by applicant]
Y. Mao, M. Wang, S. Wang, and S. Kwong, “High efficiency rate control for versatile video coding based on composite cauchy distribution,” IEEE Transactions on Circuits and Sys-tems for Video Technology, 2021. [cited by applicant]
X. Li, N. Oertel, A. Hutter, and A. Kaup, “Laplace distribution based lagrangian rate distortion optimization for hybrid video coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, No. 2, pp.… [cited by applicant]
S. Ma, W. Gao, and Y. Lu, “Rate-distortion analysis for H.264/AVC video coding and its application to rate control,” IEEE Transactions on Circuits and Systems for Video Technol-ogy, vol. 15, No. 12, pp. 1533-1544, 2005. [cited by applicant]
B. Li, H. Li, L. Li, and J. Zhang, “λ-domain rate control algorithm for high efficiency video coding,” IEEE Transactions on Image Processing, vol. 23, No. 9, pp. 3841-3854, 2014. [cited by applicant]
L. Li, B. Li, H. Li, and C. W. Chen, “λ-domain optimal bit allocation algorithm for high efficiency video coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, No. 1, pp. 130-142, 2018. [cited by applicant]
G. J. Sullivan and T. Wiegand, “Rate-distortion optimization for video compression,” IEEE Signal Processing Magazine, vol. 15, No. 6, pp. 74-90, 1998. [cited by applicant]
W. Gao, Q. Jiang, R. Wang, S. Ma, G. Li, and S. Kwong, “Consistent quality oriented rate control in HEVC via balancing intra and inter frame coding,” IEEE Transactions on Industrial Informatics, vol. 18, No. 3, pp. 1594… [cited by applicant]
M. Zhou, X. Wei, S. Kwong, W. Jia, and B. Fang, “Just noticeable distortion-based perceptual rate control in HEVC,” IEEE Transactions on Image Processing, vol. 29, pp. 7603-7614, 2020. [cited by applicant]
M. Zhou, X. Wei, S. Wang, S. Kwong, C.-K. Fong, P. H. Wong, and W. Y. Yuen, “Global rate-distortion optimization-based rate control for HEVC HDR coding,” IEEE Transactions on Circuits and Systems for Video Technology, v… [cited by applicant]
H. Guo, C. Zhu, M. Xu, and S. Li, “Inter-block dependency-based CTU level rate control for HEVC,” IEEE Transactions on Broadcasting, vol. 66, No. 1, pp. 113-126, 2019. [cited by applicant]
F. Liu and Z. Chen, “Multi-objective optimization of quality in VVC rate control for low-delay video coding,” IEEE Transactions on Image Processing, vol. 30, pp. 4706-4718, 2021. [cited by applicant]
H. Choi, J. Yoo, J. Nam, D. Sim, and I. V. Baji , “Pixel-wise unified rate-quantization model for multi-level rate control,” IEEE Journal of Selected Topics in Signal Processing, vol. 7, No. 6, pp. 1112-1123, 2013. [cited by applicant]
H.-J. Lee, T. Chiang, and Y.-Q. Zhang, “Scalable rate control for MPEG-4 video,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 10, No. 6, pp. 878-894, 2000. [cited by applicant]
Z. He, Y. K. Kim, and S. K. Mitra, “Low-delay rate control for DCT video coding via ρ-domain source modeling,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, No. 8, pp. 928-940, 2001. [cited by applicant]
Y. Li and Z. Chen, “Rate control for VVC,” JVET K0390, Jul. 2018. [cited by applicant]