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Hardware and software for multimedia systems

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: Sep. 25, 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

Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study


Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fidelity of information acquisition. Our goal in this paper is to obtain specific insights into such issues through modeling of a Large Area Electronics (LAE)-based image acquisition system.

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Authors:
Tiffany Moy, Upamanyu Madhow, Naveen Verma
Submitted On:
8 March 2017 - 3:42am
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[1] Tiffany Moy, Upamanyu Madhow, Naveen Verma, "Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1702. Accessed: Sep. 25, 2020.
@article{1702-17,
url = {http://sigport.org/1702},
author = {Tiffany Moy; Upamanyu Madhow; Naveen Verma },
publisher = {IEEE SigPort},
title = {Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study},
year = {2017} }
TY - EJOUR
T1 - Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study
AU - Tiffany Moy; Upamanyu Madhow; Naveen Verma
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1702
ER -
Tiffany Moy, Upamanyu Madhow, Naveen Verma. (2017). Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study. IEEE SigPort. http://sigport.org/1702
Tiffany Moy, Upamanyu Madhow, Naveen Verma, 2017. Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study. Available at: http://sigport.org/1702.
Tiffany Moy, Upamanyu Madhow, Naveen Verma. (2017). "Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study." Web.
1. Tiffany Moy, Upamanyu Madhow, Naveen Verma. Compressive Information Acquisition with Hardware Impairments and Constraints: A Case Study [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1702