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Image/Video Coding

ALTERNATIVE HALF-SAMPLE INTERPOLATION FILTERS FOR VERSATILE VIDEO CODING


To reduce the residual energy of a video signal, motion compensated prediction with fractional-sample accuracy has been successfully employed in modern video coding technology. In contrast to the fixed quarter-sample motion vector resolution for the luma component in High Efficiency Video Coding standard, the current draft of a new Versatile Video Coding standard introduces a block-level adaptive motion vector resolution (AMVR) scheme. The AMVR allows coding of motion vector difference at different precisions.

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Authors:
A. Henkel, I. Zupancic, B. Bross, M. Winken, H. Schwarz, D. Marpe, T. Wiegand
Submitted On:
14 May 2020 - 2:29am
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ALTERNATIVE HALF-SAMPLE INTERPOLATION FILTERS FOR VERSATILE VIDEO CODING

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[1] A. Henkel, I. Zupancic, B. Bross, M. Winken, H. Schwarz, D. Marpe, T. Wiegand, "ALTERNATIVE HALF-SAMPLE INTERPOLATION FILTERS FOR VERSATILE VIDEO CODING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5235. Accessed: Jul. 04, 2020.
@article{5235-20,
url = {http://sigport.org/5235},
author = {A. Henkel; I. Zupancic; B. Bross; M. Winken; H. Schwarz; D. Marpe; T. Wiegand },
publisher = {IEEE SigPort},
title = {ALTERNATIVE HALF-SAMPLE INTERPOLATION FILTERS FOR VERSATILE VIDEO CODING},
year = {2020} }
TY - EJOUR
T1 - ALTERNATIVE HALF-SAMPLE INTERPOLATION FILTERS FOR VERSATILE VIDEO CODING
AU - A. Henkel; I. Zupancic; B. Bross; M. Winken; H. Schwarz; D. Marpe; T. Wiegand
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5235
ER -
A. Henkel, I. Zupancic, B. Bross, M. Winken, H. Schwarz, D. Marpe, T. Wiegand. (2020). ALTERNATIVE HALF-SAMPLE INTERPOLATION FILTERS FOR VERSATILE VIDEO CODING. IEEE SigPort. http://sigport.org/5235
A. Henkel, I. Zupancic, B. Bross, M. Winken, H. Schwarz, D. Marpe, T. Wiegand, 2020. ALTERNATIVE HALF-SAMPLE INTERPOLATION FILTERS FOR VERSATILE VIDEO CODING. Available at: http://sigport.org/5235.
A. Henkel, I. Zupancic, B. Bross, M. Winken, H. Schwarz, D. Marpe, T. Wiegand. (2020). "ALTERNATIVE HALF-SAMPLE INTERPOLATION FILTERS FOR VERSATILE VIDEO CODING." Web.
1. A. Henkel, I. Zupancic, B. Bross, M. Winken, H. Schwarz, D. Marpe, T. Wiegand. ALTERNATIVE HALF-SAMPLE INTERPOLATION FILTERS FOR VERSATILE VIDEO CODING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5235

Non-Experts or Experts? Statistical Analyses of MOS using DSIS Method


In image quality assessments, the results of subjective evaluation experiments that use the double-stimulus impairment scale (DSIS) method are often expressed in terms of the mean opinion score (MOS), which is the average score of all subjects for each test condition. Some MOS values are used to derive image quality criteria, and it has been assumed that it is preferable to perform tests with non-expert subjects rather than with experts. In this study, we analyze the results of several subjective evaluation experiments using the DSIS method.

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Authors:
Marcelo Bertalmío
Submitted On:
17 April 2020 - 2:28am
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20200504_ICASSP.pdf

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[1] Marcelo Bertalmío, "Non-Experts or Experts? Statistical Analyses of MOS using DSIS Method", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5101. Accessed: Jul. 04, 2020.
@article{5101-20,
url = {http://sigport.org/5101},
author = {Marcelo Bertalmío },
publisher = {IEEE SigPort},
title = {Non-Experts or Experts? Statistical Analyses of MOS using DSIS Method},
year = {2020} }
TY - EJOUR
T1 - Non-Experts or Experts? Statistical Analyses of MOS using DSIS Method
AU - Marcelo Bertalmío
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5101
ER -
Marcelo Bertalmío. (2020). Non-Experts or Experts? Statistical Analyses of MOS using DSIS Method. IEEE SigPort. http://sigport.org/5101
Marcelo Bertalmío, 2020. Non-Experts or Experts? Statistical Analyses of MOS using DSIS Method. Available at: http://sigport.org/5101.
Marcelo Bertalmío. (2020). "Non-Experts or Experts? Statistical Analyses of MOS using DSIS Method." Web.
1. Marcelo Bertalmío. Non-Experts or Experts? Statistical Analyses of MOS using DSIS Method [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5101

Residual Coding for Transform Skip Mode in Versatile Video Coding

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Authors:
Tung Nguyen, Benjamin Bross, HeikoSchwarz,Detlev Marpe, Thomas Wiegand
Submitted On:
31 March 2020 - 4:02pm
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main-1.pdf

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[1] Tung Nguyen, Benjamin Bross, HeikoSchwarz,Detlev Marpe, Thomas Wiegand, "Residual Coding for Transform Skip Mode in Versatile Video Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5084. Accessed: Jul. 04, 2020.
@article{5084-20,
url = {http://sigport.org/5084},
author = {Tung Nguyen; Benjamin Bross; HeikoSchwarz;Detlev Marpe; Thomas Wiegand },
publisher = {IEEE SigPort},
title = {Residual Coding for Transform Skip Mode in Versatile Video Coding},
year = {2020} }
TY - EJOUR
T1 - Residual Coding for Transform Skip Mode in Versatile Video Coding
AU - Tung Nguyen; Benjamin Bross; HeikoSchwarz;Detlev Marpe; Thomas Wiegand
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5084
ER -
Tung Nguyen, Benjamin Bross, HeikoSchwarz,Detlev Marpe, Thomas Wiegand. (2020). Residual Coding for Transform Skip Mode in Versatile Video Coding. IEEE SigPort. http://sigport.org/5084
Tung Nguyen, Benjamin Bross, HeikoSchwarz,Detlev Marpe, Thomas Wiegand, 2020. Residual Coding for Transform Skip Mode in Versatile Video Coding. Available at: http://sigport.org/5084.
Tung Nguyen, Benjamin Bross, HeikoSchwarz,Detlev Marpe, Thomas Wiegand. (2020). "Residual Coding for Transform Skip Mode in Versatile Video Coding." Web.
1. Tung Nguyen, Benjamin Bross, HeikoSchwarz,Detlev Marpe, Thomas Wiegand. Residual Coding for Transform Skip Mode in Versatile Video Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5084

Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features


Modern video codecs have many compression-tuning parameters from which numerous configurations (presets) can be constructed. The large number of presets complicates the search for one that delivers optimal encoding time, quality, and compressed-video size. This paper presents a machine-learning-based method that helps to solve this problem. We applied the method to the x264 video codec: it searches for optimal presets that demonstrate 9-20% bitrate savings relative to standard x264 presets with comparable compressed-video quality and encoding time.

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Authors:
Sergey Zvezdakov, Dmitriy Vatolin
Submitted On:
24 April 2020 - 2:47pm
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Kazantsev_Zvezdakov_Vatolin_poster.pdf

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[1] Sergey Zvezdakov, Dmitriy Vatolin, "Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5082. Accessed: Jul. 04, 2020.
@article{5082-20,
url = {http://sigport.org/5082},
author = {Sergey Zvezdakov; Dmitriy Vatolin },
publisher = {IEEE SigPort},
title = {Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features},
year = {2020} }
TY - EJOUR
T1 - Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features
AU - Sergey Zvezdakov; Dmitriy Vatolin
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5082
ER -
Sergey Zvezdakov, Dmitriy Vatolin. (2020). Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features. IEEE SigPort. http://sigport.org/5082
Sergey Zvezdakov, Dmitriy Vatolin, 2020. Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features. Available at: http://sigport.org/5082.
Sergey Zvezdakov, Dmitriy Vatolin. (2020). "Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features." Web.
1. Sergey Zvezdakov, Dmitriy Vatolin. Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5082

EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images


This paper proposes extensions of CALIC for lossless compression of light field (LF) images. The overall prediction process is improved by exploiting the linear structure of Epipolar Plane Images (EPI) in a slope based prediction scheme. The prediction is improved further by averaging predictions made using horizontal and verticals EPIs. Besides this, the difference in these predictions is included in the error energy function, and the texture context is redefined to improve the overall compression ratio.

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Authors:
Muhammad Umair Mukati, Søren Forchhammer
Submitted On:
31 March 2020 - 10:27am
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PID6319313.pdf

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[1] Muhammad Umair Mukati, Søren Forchhammer, "EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5081. Accessed: Jul. 04, 2020.
@article{5081-20,
url = {http://sigport.org/5081},
author = {Muhammad Umair Mukati; Søren Forchhammer },
publisher = {IEEE SigPort},
title = {EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images},
year = {2020} }
TY - EJOUR
T1 - EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images
AU - Muhammad Umair Mukati; Søren Forchhammer
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5081
ER -
Muhammad Umair Mukati, Søren Forchhammer. (2020). EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images. IEEE SigPort. http://sigport.org/5081
Muhammad Umair Mukati, Søren Forchhammer, 2020. EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images. Available at: http://sigport.org/5081.
Muhammad Umair Mukati, Søren Forchhammer. (2020). "EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images." Web.
1. Muhammad Umair Mukati, Søren Forchhammer. EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5081

Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding

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Authors:
Shaohui Li, Wenrui Dai, Yuhui Xu, De Cheng, Gang Li, Hongkai Xiong
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31 March 2020 - 5:27am
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DCC_201_camera_ready.pdf

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[1] Shaohui Li, Wenrui Dai, Yuhui Xu, De Cheng, Gang Li, Hongkai Xiong, "Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5078. Accessed: Jul. 04, 2020.
@article{5078-20,
url = {http://sigport.org/5078},
author = {Shaohui Li; Wenrui Dai; Yuhui Xu; De Cheng; Gang Li; Hongkai Xiong },
publisher = {IEEE SigPort},
title = {Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding},
year = {2020} }
TY - EJOUR
T1 - Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding
AU - Shaohui Li; Wenrui Dai; Yuhui Xu; De Cheng; Gang Li; Hongkai Xiong
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5078
ER -
Shaohui Li, Wenrui Dai, Yuhui Xu, De Cheng, Gang Li, Hongkai Xiong. (2020). Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding. IEEE SigPort. http://sigport.org/5078
Shaohui Li, Wenrui Dai, Yuhui Xu, De Cheng, Gang Li, Hongkai Xiong, 2020. Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding. Available at: http://sigport.org/5078.
Shaohui Li, Wenrui Dai, Yuhui Xu, De Cheng, Gang Li, Hongkai Xiong. (2020). "Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding." Web.
1. Shaohui Li, Wenrui Dai, Yuhui Xu, De Cheng, Gang Li, Hongkai Xiong. Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5078

A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It

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Authors:
Yuta Nakahara, Toshiyasu Matsushima
Submitted On:
26 May 2020 - 4:06pm
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2020DCC_nakahara.pdf

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[1] Yuta Nakahara, Toshiyasu Matsushima, "A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5077. Accessed: Jul. 04, 2020.
@article{5077-20,
url = {http://sigport.org/5077},
author = {Yuta Nakahara; Toshiyasu Matsushima },
publisher = {IEEE SigPort},
title = {A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It},
year = {2020} }
TY - EJOUR
T1 - A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It
AU - Yuta Nakahara; Toshiyasu Matsushima
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5077
ER -
Yuta Nakahara, Toshiyasu Matsushima. (2020). A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It. IEEE SigPort. http://sigport.org/5077
Yuta Nakahara, Toshiyasu Matsushima, 2020. A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It. Available at: http://sigport.org/5077.
Yuta Nakahara, Toshiyasu Matsushima. (2020). "A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It." Web.
1. Yuta Nakahara, Toshiyasu Matsushima. A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5077

Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC


Rate distortion optimization (RDO) is the basis for algorithm optimization in video coding, such as mode decision, rate control and etc. Minimizing the rate distortion coding cost is usually employed to determine the optimal coding parameters such as quantization level, coding mode, and etc. However, rate and distortion calculations for optimal solution decision from massive possible candidates suffer from dramatically high computation complexity.

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Authors:
Xiaofeng Huang, Haibing Yin, Shiqi Wang
Submitted On:
31 March 2020 - 1:01am
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Statistical Modeling.pdf

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DCC poster.ppt

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[1] Xiaofeng Huang, Haibing Yin, Shiqi Wang, "Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5070. Accessed: Jul. 04, 2020.
@article{5070-20,
url = {http://sigport.org/5070},
author = {Xiaofeng Huang; Haibing Yin; Shiqi Wang },
publisher = {IEEE SigPort},
title = {Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC},
year = {2020} }
TY - EJOUR
T1 - Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC
AU - Xiaofeng Huang; Haibing Yin; Shiqi Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5070
ER -
Xiaofeng Huang, Haibing Yin, Shiqi Wang. (2020). Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC. IEEE SigPort. http://sigport.org/5070
Xiaofeng Huang, Haibing Yin, Shiqi Wang, 2020. Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC. Available at: http://sigport.org/5070.
Xiaofeng Huang, Haibing Yin, Shiqi Wang. (2020). "Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC." Web.
1. Xiaofeng Huang, Haibing Yin, Shiqi Wang. Statistical Modeling based Fast Rate Distortion Estimation Algorithm for HEVC [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5070

PERCEPTUAL VIDEO CODING USING DEEP NEURAL NETWORK BASED JND MODEL

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Authors:
Dae Yeol Lee, Seyoon Jeong, Seunghyun Cho
Submitted On:
31 March 2020 - 12:00am
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DCC2020_PERCEPTUAL VIDEO CODING USING DEEP NEURAL NETWORK BASED JND MODEL.pdf

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[1] Dae Yeol Lee, Seyoon Jeong, Seunghyun Cho, "PERCEPTUAL VIDEO CODING USING DEEP NEURAL NETWORK BASED JND MODEL", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5069. Accessed: Jul. 04, 2020.
@article{5069-20,
url = {http://sigport.org/5069},
author = {Dae Yeol Lee; Seyoon Jeong; Seunghyun Cho },
publisher = {IEEE SigPort},
title = {PERCEPTUAL VIDEO CODING USING DEEP NEURAL NETWORK BASED JND MODEL},
year = {2020} }
TY - EJOUR
T1 - PERCEPTUAL VIDEO CODING USING DEEP NEURAL NETWORK BASED JND MODEL
AU - Dae Yeol Lee; Seyoon Jeong; Seunghyun Cho
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5069
ER -
Dae Yeol Lee, Seyoon Jeong, Seunghyun Cho. (2020). PERCEPTUAL VIDEO CODING USING DEEP NEURAL NETWORK BASED JND MODEL. IEEE SigPort. http://sigport.org/5069
Dae Yeol Lee, Seyoon Jeong, Seunghyun Cho, 2020. PERCEPTUAL VIDEO CODING USING DEEP NEURAL NETWORK BASED JND MODEL. Available at: http://sigport.org/5069.
Dae Yeol Lee, Seyoon Jeong, Seunghyun Cho. (2020). "PERCEPTUAL VIDEO CODING USING DEEP NEURAL NETWORK BASED JND MODEL." Web.
1. Dae Yeol Lee, Seyoon Jeong, Seunghyun Cho. PERCEPTUAL VIDEO CODING USING DEEP NEURAL NETWORK BASED JND MODEL [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5069

Low Rate Compression of Video With Dynamic Backgrounds

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Authors:
Ryan Marcus, Antonella DiLillo, James Storer
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30 March 2020 - 8:59pm
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garber_poster_dcc_2020.pdf

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[1] Ryan Marcus, Antonella DiLillo, James Storer, "Low Rate Compression of Video With Dynamic Backgrounds", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5067. Accessed: Jul. 04, 2020.
@article{5067-20,
url = {http://sigport.org/5067},
author = {Ryan Marcus; Antonella DiLillo; James Storer },
publisher = {IEEE SigPort},
title = {Low Rate Compression of Video With Dynamic Backgrounds},
year = {2020} }
TY - EJOUR
T1 - Low Rate Compression of Video With Dynamic Backgrounds
AU - Ryan Marcus; Antonella DiLillo; James Storer
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5067
ER -
Ryan Marcus, Antonella DiLillo, James Storer. (2020). Low Rate Compression of Video With Dynamic Backgrounds. IEEE SigPort. http://sigport.org/5067
Ryan Marcus, Antonella DiLillo, James Storer, 2020. Low Rate Compression of Video With Dynamic Backgrounds. Available at: http://sigport.org/5067.
Ryan Marcus, Antonella DiLillo, James Storer. (2020). "Low Rate Compression of Video With Dynamic Backgrounds." Web.
1. Ryan Marcus, Antonella DiLillo, James Storer. Low Rate Compression of Video With Dynamic Backgrounds [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5067

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