research-article

LSIM: Ultra Lightweight Similarity Measurement for Mobile Graphics Applications

Authors:

Yu-Chuan Chang,

Tei-Wei KuoAuthors Info & Claims

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

Article No.: 25, Pages 1 - 6

https://doi.org/10.1145/3316781.3317856

Published: 02 June 2019 Publication History

Abstract

Perceptual similarity measurement allows mobile applications to eliminate unnecessary computations without compromising visual experience. Existing pixel-wise measures incur significant overhead with increasing display resolutions and frame rates. This paper presents an ultra lightweight similarity measure called LSIM, which assesses the similarity between frames based on the transformation matrices of graphics objects. To evaluate its efficacy, we integrate LSIM into the Open Graphics Library and conduct experiments on an Android smartphone with various mobile 3D games. The results show that LSIM is highly correlated with the most widely used pixel-wise measure SSIM, yet three to five orders of magnitude faster. We also apply LSIM to a CPU-GPU governor to suppress the rendering of similar frames, thereby further reducing computation energy consumption by up to 27.3% while maintaining satisfactory visual quality.

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Cited By

Hsu TChen YChen YChang YKuo T(2023)REFROM: Responsive, Energy-Efficient Frame Rendering for Mobile Devices2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)10.1109/ISLPED58423.2023.10244482(1-6)Online publication date: 7-Aug-2023
https://doi.org/10.1109/ISLPED58423.2023.10244482
Hort MKechagia MSarro FHarman M(2022)A Survey of Performance Optimization for Mobile ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2021.307119348:8(2879-2904)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3071193
Park KKim MPark L(2022)NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video StreamingIEEE Transactions on Mobile Computing10.1109/TMC.2022.3195961(1-14)Online publication date: 2022
https://doi.org/10.1109/TMC.2022.3195961

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cover image ACM Conferences

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

June 2019

1378 pages

ISBN:9781450367257

DOI:10.1145/3316781

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

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SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 June 2019

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Ministry of Science and Technology, Taiwan

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DAC '19

Sponsor:

SIGDA

DAC '19: The 56th Annual Design Automation Conference 2019

June 2 - 6, 2019

NV, Las Vegas, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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DAC '25

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sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

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Cited By

Hsu TChen YChen YChang YKuo T(2023)REFROM: Responsive, Energy-Efficient Frame Rendering for Mobile Devices2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)10.1109/ISLPED58423.2023.10244482(1-6)Online publication date: 7-Aug-2023
https://doi.org/10.1109/ISLPED58423.2023.10244482
Hort MKechagia MSarro FHarman M(2022)A Survey of Performance Optimization for Mobile ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2021.307119348:8(2879-2904)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TSE.2021.3071193
Park KKim MPark L(2022)NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video StreamingIEEE Transactions on Mobile Computing10.1109/TMC.2022.3195961(1-14)Online publication date: 2022
https://doi.org/10.1109/TMC.2022.3195961

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