TY - GEN
T1 - Enhancing Video Capture in Mobile Applications for Power Saving Through Machine Learning
AU - Chen, Tzu Heng
AU - Tsai, Sheng Da
AU - Lin, Chun Han
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/4/8
Y1 - 2024/4/8
N2 - In today's digital age, people have become accustomed to using camera applications to record and share their life experiences through videos. Enhancing the user experience of mobile devices involves a critical aspect to minimize the power consumption associated with recording videos. This paper delves into the realm of processing and displaying power-efficient videos, all recorded in real-time through camera applications. Based on pixel-scaling methods, we introduce a frame-ratio predictor, utilizing machine-learning techniques to predict frame ratios efficiently. The results, as demonstrated on a commercial smartphone using four real-world videos, are remarkably promising.
AB - In today's digital age, people have become accustomed to using camera applications to record and share their life experiences through videos. Enhancing the user experience of mobile devices involves a critical aspect to minimize the power consumption associated with recording videos. This paper delves into the realm of processing and displaying power-efficient videos, all recorded in real-time through camera applications. Based on pixel-scaling methods, we introduce a frame-ratio predictor, utilizing machine-learning techniques to predict frame ratios efficiently. The results, as demonstrated on a commercial smartphone using four real-world videos, are remarkably promising.
KW - camera applications
KW - machine-learning methods
KW - mobile devices
KW - OLED displays
KW - power-saving methods
UR - http://www.scopus.com/inward/record.url?scp=85197686110&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85197686110&partnerID=8YFLogxK
U2 - 10.1145/3605098.3636090
DO - 10.1145/3605098.3636090
M3 - Conference contribution
AN - SCOPUS:85197686110
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 1038
EP - 1039
BT - 39th Annual ACM Symposium on Applied Computing, SAC 2024
PB - Association for Computing Machinery
T2 - 39th Annual ACM Symposium on Applied Computing, SAC 2024
Y2 - 8 April 2024 through 12 April 2024
ER -