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Image/Video Coding

Residual Coding for Transform Skip Mode in Versatile Video Coding

Tung Nguyen

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Authors:
Tung Nguyen
Tung Nguyen, Benjamin Bross, HeikoSchwarz,Detlev Marpe, Thomas Wiegand
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31 March 2020 - 4:02pm
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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: Apr. 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

Roman Kazantsev

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:
Roman Kazantsev
Sergey Zvezdakov, Dmitriy Vatolin
Submitted On:
1 April 2020 - 11:17am
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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: Apr. 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

Muhammad Umair Mukati

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

Jixiang Luo

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Authors:
Jixiang Luo
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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[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: Apr. 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

Yuta Nakahara

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Authors:
Yuta Nakahara
Yuta Nakahara, Toshiyasu Matsushima
Submitted On:
31 March 2020 - 5:37am
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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: Apr. 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

Xiang Meng

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:
Xiang Meng
Xiaofeng Huang, Haibing Yin, Shiqi Wang
Submitted On:
31 March 2020 - 1:01am
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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: Apr. 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

Jongho Kim

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Authors:
Jongho Kim
Dae Yeol Lee, Seyoon Jeong, Seunghyun Cho
Submitted On:
31 March 2020 - 12:00am
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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: Apr. 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

Solomon Garber

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Authors:
Solomon Garber
Ryan Marcus, Antonella DiLillo, James Storer
Submitted On:
30 March 2020 - 8:59pm
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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: Apr. 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

Artificial Intelligence based region of interest enhanced video compression

PALANIVEL GURUVAREDDIAR

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Authors:
PALANIVEL GURUVAREDDIAR
Palanivel Guruvareddiar, Praveen Prasad
Submitted On:
30 March 2020 - 3:35pm
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[1] Palanivel Guruvareddiar, Praveen Prasad, "Artificial Intelligence based region of interest enhanced video compression", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5066. Accessed: Apr. 04, 2020.
@article{5066-20,
url = {http://sigport.org/5066},
author = {Palanivel Guruvareddiar; Praveen Prasad },
publisher = {IEEE SigPort},
title = {Artificial Intelligence based region of interest enhanced video compression},
year = {2020} }
TY - EJOUR
T1 - Artificial Intelligence based region of interest enhanced video compression
AU - Palanivel Guruvareddiar; Praveen Prasad
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5066
ER -
Palanivel Guruvareddiar, Praveen Prasad. (2020). Artificial Intelligence based region of interest enhanced video compression. IEEE SigPort. http://sigport.org/5066
Palanivel Guruvareddiar, Praveen Prasad, 2020. Artificial Intelligence based region of interest enhanced video compression. Available at: http://sigport.org/5066.
Palanivel Guruvareddiar, Praveen Prasad. (2020). "Artificial Intelligence based region of interest enhanced video compression." Web.
1. Palanivel Guruvareddiar, Praveen Prasad. Artificial Intelligence based region of interest enhanced video compression [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5066

State-Based Multi-Parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding

Paul Haase

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Authors:
Paul Haase
Paul Haase, Stefan Matlage, Heiner Kirchhoffer, Christian Bartnik, Heiko Schwarz, Detlev Marpe, Thomas Wiegand
Submitted On:
30 March 2020 - 10:27am
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[1] Paul Haase, Stefan Matlage, Heiner Kirchhoffer, Christian Bartnik, Heiko Schwarz, Detlev Marpe, Thomas Wiegand, "State-Based Multi-Parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5062. Accessed: Apr. 04, 2020.
@article{5062-20,
url = {http://sigport.org/5062},
author = {Paul Haase; Stefan Matlage; Heiner Kirchhoffer; Christian Bartnik; Heiko Schwarz; Detlev Marpe; Thomas Wiegand },
publisher = {IEEE SigPort},
title = {State-Based Multi-Parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding},
year = {2020} }
TY - EJOUR
T1 - State-Based Multi-Parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding
AU - Paul Haase; Stefan Matlage; Heiner Kirchhoffer; Christian Bartnik; Heiko Schwarz; Detlev Marpe; Thomas Wiegand
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5062
ER -
Paul Haase, Stefan Matlage, Heiner Kirchhoffer, Christian Bartnik, Heiko Schwarz, Detlev Marpe, Thomas Wiegand. (2020). State-Based Multi-Parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding. IEEE SigPort. http://sigport.org/5062
Paul Haase, Stefan Matlage, Heiner Kirchhoffer, Christian Bartnik, Heiko Schwarz, Detlev Marpe, Thomas Wiegand, 2020. State-Based Multi-Parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding. Available at: http://sigport.org/5062.
Paul Haase, Stefan Matlage, Heiner Kirchhoffer, Christian Bartnik, Heiko Schwarz, Detlev Marpe, Thomas Wiegand. (2020). "State-Based Multi-Parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding." Web.
1. Paul Haase, Stefan Matlage, Heiner Kirchhoffer, Christian Bartnik, Heiko Schwarz, Detlev Marpe, Thomas Wiegand. State-Based Multi-Parameter Probability Estimation for Context-Based Adaptive Binary Arithmetic Coding [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5062

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