An Image Compression Scheme in Wireless Multimedia Sensor Networks Based on NMF
AbstractWith the goal of addressing the issue of image compression in wireless multimedia sensor networks with high recovered quality and low energy consumption, an image compression and transmission scheme based on non-negative matrix factorization (NMF) is proposed in this paper. First, the NMF algorithm theory is studied. Then, a collaborative mechanism of image capture, block, compression and transmission is completed. Camera nodes capture images and send them to ordinary nodes which use an NMF algorithm for image compression. Compressed images are transmitted to the station by the cluster head node and received from ordinary nodes. The station takes on the image restoration. Simulation results show that, compared with the JPEG2000 and singular value decomposition (SVD) compression schemes, the proposed scheme has a higher quality of recovered images and lower total node energy consumption. It is beneficial to reduce the burden of energy consumption and prolong the life of the whole network system, which has great significance for practical applications of WMSNs. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Kong, S.; Sun, L.; Han, C.; Guo, J. An Image Compression Scheme in Wireless Multimedia Sensor Networks Based on NMF. Information 2017, 8, 26.
Kong S, Sun L, Han C, Guo J. An Image Compression Scheme in Wireless Multimedia Sensor Networks Based on NMF. Information. 2017; 8(1):26.Chicago/Turabian Style
Kong, Shikang; Sun, Lijuan; Han, Chong; Guo, Jian. 2017. "An Image Compression Scheme in Wireless Multimedia Sensor Networks Based on NMF." Information 8, no. 1: 26.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.