Abstract
With increase demand on wireless services, equipment supporting multimedia applications has been becoming more and more popular in recent years. With billions of devices involved in mobile Internet, data volume is undergoing an extremely rapid growth. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive study has been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this paper, a hybrid-stream big data analytics model is proposed to perform big data video analysis. This model contains four procedures, i.e., data preprocessing, data classification, data recognition and data load reduction. Specifically, an innovative multi-dimensional Convolution Neural Network (CNN) is proposed to obtain the importance of each video frame. Thus, those unimportant frames can be dropped by a reliable decision-making algorithm. Then, a reliable key frame extraction mechanism will recognize the importance of each frame or clip and then decide whether to abandon it automatically by a series of correlation operations. Simulation results illustrate that the size of the processed video has been effectively reduced. The simulation also shows that proposed model performs steadily and is robust enough to keep up with the big data crush in multimedia era.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE/CIC International Conference on Communications in China, ICCC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509021437 |
| DOIs | |
| Publication status | Published - 2016 Oct 21 |
| Event | 2016 IEEE/CIC International Conference on Communications in China, ICCC 2016 - Chengdu, China Duration: 2016 Jul 27 → 2016 Jul 29 |
Publication series
| Name | 2016 IEEE/CIC International Conference on Communications in China, ICCC 2016 |
|---|
Other
| Other | 2016 IEEE/CIC International Conference on Communications in China, ICCC 2016 |
|---|---|
| Country | China |
| City | Chengdu |
| Period | 16-07-27 → 16-07-29 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Signal Processing
Cite this
}
Real-time big data analytics for multimedia transmission and storage. / Wang, Kun; Mi, Jun; Xu, Chenhan; Shu, Lei; Deng, Der-Jiunn.
2016 IEEE/CIC International Conference on Communications in China, ICCC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7636815 (2016 IEEE/CIC International Conference on Communications in China, ICCC 2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Real-time big data analytics for multimedia transmission and storage
AU - Wang, Kun
AU - Mi, Jun
AU - Xu, Chenhan
AU - Shu, Lei
AU - Deng, Der-Jiunn
PY - 2016/10/21
Y1 - 2016/10/21
N2 - With increase demand on wireless services, equipment supporting multimedia applications has been becoming more and more popular in recent years. With billions of devices involved in mobile Internet, data volume is undergoing an extremely rapid growth. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive study has been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this paper, a hybrid-stream big data analytics model is proposed to perform big data video analysis. This model contains four procedures, i.e., data preprocessing, data classification, data recognition and data load reduction. Specifically, an innovative multi-dimensional Convolution Neural Network (CNN) is proposed to obtain the importance of each video frame. Thus, those unimportant frames can be dropped by a reliable decision-making algorithm. Then, a reliable key frame extraction mechanism will recognize the importance of each frame or clip and then decide whether to abandon it automatically by a series of correlation operations. Simulation results illustrate that the size of the processed video has been effectively reduced. The simulation also shows that proposed model performs steadily and is robust enough to keep up with the big data crush in multimedia era.
AB - With increase demand on wireless services, equipment supporting multimedia applications has been becoming more and more popular in recent years. With billions of devices involved in mobile Internet, data volume is undergoing an extremely rapid growth. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive study has been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this paper, a hybrid-stream big data analytics model is proposed to perform big data video analysis. This model contains four procedures, i.e., data preprocessing, data classification, data recognition and data load reduction. Specifically, an innovative multi-dimensional Convolution Neural Network (CNN) is proposed to obtain the importance of each video frame. Thus, those unimportant frames can be dropped by a reliable decision-making algorithm. Then, a reliable key frame extraction mechanism will recognize the importance of each frame or clip and then decide whether to abandon it automatically by a series of correlation operations. Simulation results illustrate that the size of the processed video has been effectively reduced. The simulation also shows that proposed model performs steadily and is robust enough to keep up with the big data crush in multimedia era.
UR - http://www.scopus.com/inward/record.url?scp=84997815804&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84997815804&partnerID=8YFLogxK
U2 - 10.1109/ICCChina.2016.7636815
DO - 10.1109/ICCChina.2016.7636815
M3 - Conference contribution
AN - SCOPUS:84997815804
T3 - 2016 IEEE/CIC International Conference on Communications in China, ICCC 2016
BT - 2016 IEEE/CIC International Conference on Communications in China, ICCC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -