MoEnlight: Energy-efficient and self-Adaptive Low-light Video Stream Enhancement on Mobile Devices

Sicong Liu; Xiaochen Li; Zimu Zhou; Bin Guo; Yuan Xu; Zhiwen Yu

doi:10.1145/3603165.3607375

MoEnlight: Energy-efficient and self-Adaptive Low-light Video Stream Enhancement on Mobile Devices

Sicong Liu
, Xiaochen Li
, Zimu Zhou
, Bin Guo
, Yuan Xu
, Zhiwen Yu

School of Computer Science

Northwestern Polytechnical University Xian
City University of Hong Kong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Camera-equipped devices and deep learning advancements have driven the development of intelligent mobile video apps. These apps require on-device processing of video streams for real-Time, high-quality services while addressing privacy and robustness. However, their performance is limited by low-light conditions and small-Aperture cameras in mobile platforms. Existing low-light video enhancement solutions are unsuitable due to complex models and lack of energy efficiency. We introduce MoEnlight, an energy-conscious system for enhancing low-light video on mobile devices. MoEnlight achieves real-Time enhancement with competitive quality, adapting to dynamic energy budgets. Our experiments demonstrate MoEnlight's superiority over state-of-The-Art solutions for enhancing low-light videos.

Original language	English
Title of host publication	Proceedings of ACM Turing Award Celebration Conference, CHINA 2023
Publisher	Association for Computing Machinery, Inc
Pages	19-20
Number of pages	2
ISBN (Electronic)	9798400702334
DOIs	https://doi.org/10.1145/3603165.3607375
State	Published - 28 Jul 2023
Event	2023 ACM Turing Award Celebration Conference, CHINA 2023 - Wuhan, China Duration: 28 Jul 2023 → 30 Jul 2023

Publication series

Name	Proceedings of ACM Turing Award Celebration Conference, CHINA 2023

Conference

Conference	2023 ACM Turing Award Celebration Conference, CHINA 2023
Country/Territory	China
City	Wuhan
Period	28/07/23 → 30/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

energy awareness
low light video enhancement
mobile devices

Access to Document

10.1145/3603165.3607375

Cite this

Liu, S., Li, X., Zhou, Z., Guo, B., Xu, Y., & Yu, Z. (2023). MoEnlight: Energy-efficient and self-Adaptive Low-light Video Stream Enhancement on Mobile Devices. In Proceedings of ACM Turing Award Celebration Conference, CHINA 2023 (pp. 19-20). (Proceedings of ACM Turing Award Celebration Conference, CHINA 2023). Association for Computing Machinery, Inc. https://doi.org/10.1145/3603165.3607375

@inproceedings{43dcb8d56fb34bdcbc286f2e097574df,

title = "MoEnlight: Energy-efficient and self-Adaptive Low-light Video Stream Enhancement on Mobile Devices",

abstract = "Camera-equipped devices and deep learning advancements have driven the development of intelligent mobile video apps. These apps require on-device processing of video streams for real-Time, high-quality services while addressing privacy and robustness. However, their performance is limited by low-light conditions and small-Aperture cameras in mobile platforms. Existing low-light video enhancement solutions are unsuitable due to complex models and lack of energy efficiency. We introduce MoEnlight, an energy-conscious system for enhancing low-light video on mobile devices. MoEnlight achieves real-Time enhancement with competitive quality, adapting to dynamic energy budgets. Our experiments demonstrate MoEnlight's superiority over state-of-The-Art solutions for enhancing low-light videos.",

keywords = "energy awareness, low light video enhancement, mobile devices",

author = "Sicong Liu and Xiaochen Li and Zimu Zhou and Bin Guo and Yuan Xu and Zhiwen Yu",

note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 2023 ACM Turing Award Celebration Conference, CHINA 2023 ; Conference date: 28-07-2023 Through 30-07-2023",

year = "2023",

month = jul,

day = "28",

doi = "10.1145/3603165.3607375",

language = "英语",

series = "Proceedings of ACM Turing Award Celebration Conference, CHINA 2023",

publisher = "Association for Computing Machinery, Inc",

pages = "19--20",

booktitle = "Proceedings of ACM Turing Award Celebration Conference, CHINA 2023",

}

Liu, S, Li, X, Zhou, Z, Guo, B, Xu, Y & Yu, Z 2023, MoEnlight: Energy-efficient and self-Adaptive Low-light Video Stream Enhancement on Mobile Devices. in Proceedings of ACM Turing Award Celebration Conference, CHINA 2023. Proceedings of ACM Turing Award Celebration Conference, CHINA 2023, Association for Computing Machinery, Inc, pp. 19-20, 2023 ACM Turing Award Celebration Conference, CHINA 2023, Wuhan, China, 28/07/23. https://doi.org/10.1145/3603165.3607375

MoEnlight: Energy-efficient and self-Adaptive Low-light Video Stream Enhancement on Mobile Devices. / Liu, Sicong; Li, Xiaochen; Zhou, Zimu et al.
Proceedings of ACM Turing Award Celebration Conference, CHINA 2023. Association for Computing Machinery, Inc, 2023. p. 19-20 (Proceedings of ACM Turing Award Celebration Conference, CHINA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - MoEnlight

T2 - 2023 ACM Turing Award Celebration Conference, CHINA 2023

AU - Liu, Sicong

AU - Li, Xiaochen

AU - Zhou, Zimu

AU - Guo, Bin

AU - Xu, Yuan

AU - Yu, Zhiwen

PY - 2023/7/28

Y1 - 2023/7/28

N2 - Camera-equipped devices and deep learning advancements have driven the development of intelligent mobile video apps. These apps require on-device processing of video streams for real-Time, high-quality services while addressing privacy and robustness. However, their performance is limited by low-light conditions and small-Aperture cameras in mobile platforms. Existing low-light video enhancement solutions are unsuitable due to complex models and lack of energy efficiency. We introduce MoEnlight, an energy-conscious system for enhancing low-light video on mobile devices. MoEnlight achieves real-Time enhancement with competitive quality, adapting to dynamic energy budgets. Our experiments demonstrate MoEnlight's superiority over state-of-The-Art solutions for enhancing low-light videos.

AB - Camera-equipped devices and deep learning advancements have driven the development of intelligent mobile video apps. These apps require on-device processing of video streams for real-Time, high-quality services while addressing privacy and robustness. However, their performance is limited by low-light conditions and small-Aperture cameras in mobile platforms. Existing low-light video enhancement solutions are unsuitable due to complex models and lack of energy efficiency. We introduce MoEnlight, an energy-conscious system for enhancing low-light video on mobile devices. MoEnlight achieves real-Time enhancement with competitive quality, adapting to dynamic energy budgets. Our experiments demonstrate MoEnlight's superiority over state-of-The-Art solutions for enhancing low-light videos.

KW - energy awareness

KW - low light video enhancement

KW - mobile devices

UR - https://www.scopus.com/pages/publications/85174262591

U2 - 10.1145/3603165.3607375

DO - 10.1145/3603165.3607375

M3 - 会议稿件

AN - SCOPUS:85174262591

T3 - Proceedings of ACM Turing Award Celebration Conference, CHINA 2023

SP - 19

EP - 20

BT - Proceedings of ACM Turing Award Celebration Conference, CHINA 2023

PB - Association for Computing Machinery, Inc

Y2 - 28 July 2023 through 30 July 2023

ER -

MoEnlight: Energy-efficient and self-Adaptive Low-light Video Stream Enhancement on Mobile Devices

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this