Personalized and efficient social image transmission scheme in mobile wireless network

Abstract

This paper presents a Personalized large Social Image Transmission method in mobile wireless network(MWN) environment, called the PSIT. The whole transmission process of the PSIT works as follows: first, when a social image I S is prepared to transmit from a sender node to user U R , a preprocessing step is then conducted to obtain the optimal image fragment(IF) replica based on the users' preference model and the network bandwidth at the sender node. After that, the candidate IFs are transferred to the receiver node from the slave one according to the transmission priorities. Finally, the IFs can be recovered and displayed at the receiver node level. The proposed method includes five enabling techniques: 1) neighborhood-based tag enrichment processing, 2) user attention degree(UAD) derivation of the regions of interest(ROI), 3) an adaptive multi-resolution-based IF replica selection method, 4) a UAD-based IF replica placement method, and 5) a priority-based robust IF transmission scheme. The experimental results show that the performance of our approach is both efficient and effective, minimizing the response time by decreasing the network transmission cost while increasing the parallelism of I/O and CPU.

Analysis of Image Transmission using MIMO-Alamouti Space-Time Encoding

“…Now at the receiver end, Zero-forcing equalizer is used followed by the demodulation using MPSK demodulator for recovery of a transmitted image, i.e., the recovery procedure of a transmitted image is done using the inverse procedure of transmission. Hence, the BER analysis is done for the transmission and reception of an image using different values of M [5], [8], [13]. The representation of SNR vs. BER plot of MIMO system for image transmission with MPSK modulation over Rayleigh channel for all antenna architectures using Alamouti MIMO channel model with zero forcing detectors is given in Figure 4.…”

Multiple transmission optimization of medical images in recourse-constraint mobile telemedicine systems

“…To achieve a faster transmission performance, Chang et al [14] In our previous work, Zhuang et al [3] have explored the feasibility of content-aware and multi-resolution-based medical image transmission scheme in which only two factors, namely, the image content and the network bandwidth are considered to optimize the transmission processing. Based on the above transmission model, Zhuang et al [28] designed a personalized social image transmission scheme in mobile wireless network.…”