KFU. Card publication. Hardware and software video encoding comparison

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

HARDWARE AND SOFTWARE VIDEO ENCODING COMPARISON

Form of presentation

Conference proceedings in international journals and collections

Year of publication

2020

Authors, employees of KFU

Safin Ramil Nabiullovich, author

Other authors

H. Li, M. Svinin, E. Garipova

Authors, postgraduates KFU

Safin Ramil Nabiullovich, postgraduate kfu

Bibliographic description in the original language

R. Safin, E. Garipova, R. Lavrenov, H. Li, M. Svinin and E. Magid, "Hardware and software video encoding comparison," 2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Chiang Mai, Thailand, 2020, pp. 924-929.

Annotation

Long time of data encoding with software encoding mechanisms might become a significant problem when transferring digital data from cameras of mobile robots. At the same time, processor manufacturers claim that an encoding process is significantly accelerated by using a hardware encoding. This work is dedicated to a hardware and a software video encoding comparison of two state-of-the-art codecs, which were selected due to their high popularity in computer vision and robotics fields - a hardware encoding with h264 vaapi and a software encoding with FFmpeg API libx265 codec. We encoded six video sequences of different resolutions and sizes with the two codecs and evaluated obtained video quality using the Structural Similarity Index, the Peak Signal-to-Noise ratio, and the Video Multimethod Assessment Fusion metrics. Software and hardware encoding processes were also compared by CPU and memory usage, and time that was taken by the encoding process. Our results demonstrated that the hard

Keywords

Computer vision, Robot vision, Video stream, Hardware encoding, Software encoding, Intel QuickSync,FFmpeg

URL

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9240439

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=250503&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Safin Ramil Nabiullovich	ru_RU
dc.contributor.author	Safin Ramil Nabiullovich	ru_RU
dc.date.accessioned	2020-01-01T00:00:00Z	ru_RU
dc.date.available	2020-01-01T00:00:00Z	ru_RU
dc.date.issued	2020	ru_RU
dc.identifier.citation	R. Safin, E. Garipova, R. Lavrenov, H. Li, M. Svinin and E. Magid, "Hardware and software video encoding comparison," 2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Chiang Mai, Thailand, 2020, pp. 924-929.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=250503&p_lang=2	ru_RU
dc.description.abstract	Long time of data encoding with software encoding mechanisms might become a significant problem when transferring digital data from cameras of mobile robots. At the same time, processor manufacturers claim that an encoding process is significantly accelerated by using a hardware encoding. This work is dedicated to a hardware and a software video encoding comparison of two state-of-the-art codecs, which were selected due to their high popularity in computer vision and robotics fields - a hardware encoding with h264 vaapi and a software encoding with FFmpeg API libx265 codec. We encoded six video sequences of different resolutions and sizes with the two codecs and evaluated obtained video quality using the Structural Similarity Index, the Peak Signal-to-Noise ratio, and the Video Multimethod Assessment Fusion metrics. Software and hardware encoding processes were also compared by CPU and memory usage, and time that was taken by the encoding process. Our results demonstrated that the hard	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Computer vision	ru_RU
dc.subject	Robot vision	ru_RU
dc.subject	Video stream	ru_RU
dc.subject	Hardware encoding	ru_RU
dc.subject	Software encoding	ru_RU
dc.subject	Intel QuickSync	ru_RU
dc.subject	FFmpeg	ru_RU
dc.title	Hardware and software video encoding comparison	ru_RU
dc.type	Conference proceedings in international journals and collections	ru_RU