An analysis of image compression techniques in wireless multimedia sensor networks

Abstract

Subject reviewWireless Multimedia Sensor Networks (WMSNs) provide realization of applications which are usable everywhere and address many fields like mobile health care, environmental surveillance and traffic monitoring. Large amount of data causes to traffic in memory resources, difficulties in operation, and excessive power consumption -which is the most important one -for every node while WMSNs transfer multimedia data during those applications. Those kinds of problems are vital for WMSNs which already have limited resources. Image compression can be one of the effective solutions to overcome those problems. Thus, network lifetime of WMSNs can be increased significantly and the bandwidth can be used in a more effective way. The main purpose of this study is to investigate image compression algorithms used for WMSNs in the literature and to show which algorithm is advantageous in which case by making comparisons among them. Keywords: energy efficiency; image compression; wireless multimedia sensor networkAnaliza načina sažimanja slike u bežičnoj višemedijskoj senzorskoj mreži Pregledni članakBežične višemedijske senzorske mreže -Wireless Multimedia Sensor Networks (WMSNs) omogućuju realizaciju aplikacija koje se svugdje mogu primijeniti, a odnose se na mnoga područja kao što je briga o zdravlju, praćenje stanja okoliša i upravljanje prometom. Izmjena velikih količina podataka stvara poteškoće u funkcioniranju memorije te prekomjernu potrošnju energije u svakom čvoru -a to je najvažnije -dok WMSNs prenose multimedijske podatke tijekom tih aplikacija. Ta je vrsta problema od bitne važnosti za WMSNs koji već imaju ograničene resurse. Sažimanje slike može biti jedno od učinkovitih rješenja za svladavanje tih problema. Trajnost WMSNs na mreži može se tako značajno povećati, a širina područja se može učinkovitije iskoristiti. Glavna svrha ovoga rada je istražiti algoritme sažimanja slike koji se u literaturi primjenjuju za WMSNs te uspoređujući ih pokazati koji algoritam ima prednost u pojedinom slučaju.

Accelerated Proximal Algorithm for Finding the Dantzig Selector and Source Separation Using Dictionary Learning

“…Fourier transform, wavelet transform, sine and cosine transforms etc. In CS, original signal is recovered from far fewer samples as compared to the conventional Shannon theory [1][2][3][4][5]. Most of the practical signals are sparse in one or other domain, for example the sinusoid shaped signals are mostly sparse in the Fourier domain unlike images which have sparse representation in cosine domain.…”