Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

Vũ Hữu Tiến, Thao Nguyen Thi Huong, San Vu Van, Xiem HoangVan


Transform domain Wyner-Ziv video coding (TDWZ) has shown its benefits in compressing video applications with limited resources such as visual surveillance systems, remote sensing and wireless sensor networks. In TDWZ, the correlation noise model (CNM) plays a vital role since it directly affects to the number of bits needed to send from the encoder and thus the overall TDWZ compression performance. To achieve CNM with high accurate for TDWZ, we propose in this paper a novel CNM estimation approach in which the CNM with Laplacian distribution is adaptively estimated based on a deep learning (DL) mechanism. The proposed DL based CNM includes two hidden layers and a linear activation function to adaptively update the Laplacian parameter. Experimental results showed that the proposed TDWZ codec significantly outperforms the relevant benchmarks, notably by around 35% bitrate saving when compared to the DISCOVER codec and around 22% bitrate saving when compared to the HEVC Intra benchmark while providing a similar perceptual quality.

Full Text:



T. Wiegand et al, Overview of the H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology vol. 13, no. 7 2003, 560 – 576.

G.J. Sullivan et al, Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Transactions on Circuits and Systems for Video Technology vol. 22, no. 12 2012, 1649 – 1668.

D. Slepian and J. Wolf, Noiseless coding of correlated information sources. IEEE Transactions on Information Theory, vol. 19, no. 4 1973, 471 – 480.

A. Wyner and J. Ziv, The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory, vol. 22, no. 1 1976, 1 – 10.

R.Puri, A.Majumdar, and K.Ramchandran, PRISM: a video coding paradigm with motion estimation at the decoder, IEEE Transactions on Image Processing, vol.16, no.10 2007, 2436 – 2448.

A. Aaron, S. Rane, E. Setton, B. Girod, Transform domain Wyner–Ziv codec for video. in:Proceedings of SPIEVCIP 2004, 1, 520 – 528.

X. Artigas, J. Ascenso, M. Dalai, S. Klomp, D. Kubasov, and M.Ouaret, The DISCOVER codec: Architecture, techniques and evaluation, Proc. of Picture Coding Symposium 2007, 10, 1 – 4.

S. Milani and G. Calvagno, A distributed video coder based on the H.264/AVC standard, 15th European Signal Processing Conference, Poznan, Poland 2007, 9.

C. Brites, J. Ascenso, F. Pereira, Improving transform domain Wyner–Ziv video coding performance. in:IEEE International Conference on Acoustics, Speech, and Signal Processing 2006, 5, 525 – 528.

R. Martins, C. Brites, J. Ascenso, F. Pereira, Statistical motion learning for improved transform domain Wyner–Ziv video coding. Image Process.4(1) 2010, 1, 28 – 41.

C. Brites, J. Ascenso, F. Pereira, Studying temporal correlation noise modeling for pixel based Wyner–Ziv video coding. in: IEEE International Conference on Image Processing 2006, 10, 273 – 276.

X. HoangVan et al., Correlation modeling for a distributed scalalbe video codec based on the HEVC standard, in IEEE MMSP, Jakarta, Indonesia 2014, 9.

X. HoangVan et al., HEVC backward compatible scalability: A low encoding complexity distributed video coding based approach, Signal Process.: Image Commun., vol. 33, no. 4 2015, 4, 51 – 70.

X. HoangVan et al, Adaptive Scalable Video Coding: An HEVC-Based Framework Combining the Predictive and Distributed Paradigms, IEEE Transactions on Circuits and Systems for Video Technology, Volume: 27 , Issue: 8 2017}, 8,1761 – 1776.

C. Brites, F. Pereira, Correlation noise modelling for efficient pixel and transform domain Wyner–Ziv video coding, IEEE Trans. Circuits Syst.Video Technol.18(9) 2008, 9, 1177 – 1190.

Brites, C., Ascenso, J., Pereira, F. (2012). Learning based decoding approach for improved Wyner-Ziv video coding, Picture Coding 2012, 3, 165 – 168.

JThomas Maugey, Jerome Gauthier, Beatrice Pesquet-Popescu, Using an exponential power model for Wyner Ziv video coding, IEEE International Conference on Acoustics Speech and Signal Processing,Dallas, TX 2010, 3, 2338 – 2341.

Hao Qin, Bin Song, Yue Zhao, and Haihua Liu, Adaptive Correlation Noise Model for DC Coefficients in Wyner-Ziv Video Coding, ETRI Journal, Volume 34, Number 2 2008, 4, 1177 – 1190.

J. Park, B. Jeon, D. Wang, and A. Vincent, Wyner-Ziv video coding with region adaptive quantization and progressive channel noise modeling, in Proc. IEEE Int. Symp. Broadband Multimedia Syst. Broadcast. (BMSB), Bilbao, Spain 2009, 5, 1 – 6.

H. V. Luong, X. Huang, and S. Forchhammer, Adaptive noise model for transform domain Wyner-Ziv video using clustering of DCT blocks, in Proc. IEEE Int. Workshop Multimedia Signal, Hangzhou, China 2011, 10, 1 – 6.

T. Chen, H. Liu, Q. Shen, T. Yue, X. Cao, and Z. Ma, Deep coder: A deep neural network based video compression, In VCIP IEEE 2017, 1 – 4.

R. Song, D. Liu, H. Li, and F. Wu, Neural network-based arithmetic coding of intra prediction modes in hevc, In VCIP IEEE 2017, 1 – 4.

Tian, Bo and Xiong, Weizhi, A Side Information Generation method using Deep Learning for Distributed Video Coding. Journal of Physics: Conference Series 2018, 1 – 6.

Dash, B., Rup, S., Mohapatra, A. et al, Multi-resolution extreme learning machine-based side information estimation in distributed video coding, Multimed Tools Appl 77 2018, 27301 – 27335.

R. Martins, C. Brites, J. Ascenso, F. Pereira, Refining side information for improved transform domain Wyner–Ziv video coding, IEEE Transactions on Circuits and Systems for Video Technology 19 (9) 2009, 1327 – 1341.

Jani Lainema, Frank Bossen, Woo-Jin Han, Junghye Min, Kemal Ugur, Intra Coding of the HEVC Standard, IEEE Transactions on Circuits and Systems for Video Technology, Volume: 22, Issue: 12 2012, 12, 1792 – 1801.

Brites, Catarina and Pereira, Fernando, Distributed video coding: Assessing the HEVC upgrade, Signal Processing: Image Communication 32 2015, 81 – 105.

Google, Colaboratory: frequently asked questions, 2018, [Access: 6-21-2018]. [Online]. Available: http-s://

HEVC reference software, [Online]. Available:

Thao Nguyen Thi Huong, Tien Vu Huu, Minh Nguyen Ngoc, Xiem HoangVan, Improving performance of distributed video coding by consecutively refining of side information and correlation noise model, 19th International Symposium on Communications and Information Technologies (ISCIT) 2019, 9, 502 – 506.

G. Bjontegaard, Calculation of average PSNR differences between RD curves, Doc. VCEG-M33, 13th ITU-T VCEG Meeting, Austin, TX, USA 2001, 4.


Copyright (c) 2020 REV Journal on Electronics and Communications

Copyright © 2011-2024
Radio and Electronics Association of Vietnam
All rights reserved