Abstract
This paper presents a Personalized large Social Image Transmission method in mobile wireless network(MWN) environment, called the P sit. The whole transmission process of the P sit 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.
Similar content being viewed by others
Notes
Available at http://www.flickr.com
Available at http://www.youtube.com
The two images(i.e., I 3 and I′ 4) are similar if their similarity distance is less than a threshold value ε set by user.
Strictly speaking, the pixel resolutions of the three ROIs in Fig. 8b are different based on the user preferences.
References
Allcocka B, Bestera J, Bresnahan J et al (2002) Data management and transfer in high-performance computational grid environments. Parallel Comput 28(5):749–771
Aziz SM, Pham DM (June) Energy efficient image transmission in wireless multimedia sensor networks. IEEE Commun Lett 17(6):1084–1087
Buyya R, Pathan M, Vakali A (2008) Content delivery networks. Springer
Chang RC, Shih TK, Hsu HH (2008) A strategic decomposition for adaptive image transmission. J Inf Sci Eng 24(3):691–707
Chang CC, Shih TK, Lin IC (2002) An efficient progressive image transmission method based on guessing by neighbors.Vis Comput Int J Comput Graph (18):341–353
Chang CC, Shine FC, Chen TS (1999) A new scheme of progressive image transmission based on bit-plane method. Asia Pac Conf Commun Fourth Optoelectron Commun Conf 2:892–895
Chang CC, Wu MN (2003) A color image progressive transmission method by common bit map block truncation coding approach, In International Conference on Communication Technology (2):1774–1778
Charles JT, Larry LP (1992) Image transfer: an end-to-end design. In ACM SIGCOMM International Conference on Data Communication, 258–268
Chua T-S, Tang J, Hong R, Li H, Luo Z, Zheng Y-T (2009) NUS-WIDE: A Real-World Web Image Database from National University of Singapore. In: ACM Int’l Conference on Image and Video Retrieval
Gao DH, Liu DH, Feng YQ et al (2010) A robust image transmission scheme for wireless channels based on compressive sensing. Advanced intelligent computing theories and applications. With aspects of artificial intelligence. Lect Notes Comput Sci 6216:334–341
John MD, Georey MD, Song XY (1995) Fast lossy internet image transmission. In ACM Int’l Conference on Multimedia
Kim JH, Song WJ (1996) Pyramid-structured progressive image transmission using quantization error delivery in transform domains. IEE Vis Image Signal Process. (143):132–136
Li X, Snoek C, Worring M, Smeulders A (2012) Harvesting social images for biconcept search. IEEE Trans Multimed 14(4):1091–1104
Li X, Snoek C, Worring M (2009) Learning social tag relevance by neighbor voting. IEEE Trans Multimed 11(7):1310–1322
Lin T, Hao P (2005) Compound image compression for real-time computer screen image transmission. IEEE Trans Image Process 14(8):993–1005
MySQL (2010). http://www.mysql.com/
Panoramio (2009). http://www.panoramio.com/
Paul S, Fei Z (2001) Distributed caching with centralized control. Comput Commun 24(2):256–268
Pham DM, Aziz SM (2013) An energy efficient image compression scheme for wireless sensor networks, proceedings of the IEEE eighth international conference on intelligent sensors. Sensor Networks and Information Processing (ISSNIP), Melbourne, pp 260–264
Pham DM, Aziz SM (2013) Object extraction scheme and protocol for energy efficient image communication over wireless sensor networks. Comput Netw Elsevier 57(15):2949–2960
Pinar Sarisaray Boluk, Sebnem Baydere, Emre Harmanci A (2011) Robust image transmission over wireless sensor networks. J Mob Netw Appl Arch 16(2):149–170
Rabbat R (2010) Web P a new image format for the Web. Chromium Blog. Google. Retrieved. 10-01
Raman S, Balakrishnan H, Srinivasan M (2000) An image transport protocol for the Internet. In International Conference on Network Protocol, 209–219
Ruiz VG, Fernández JJ, García I (2001) Image compression for progressive transmission. In the Nineteenth IASTED International Conference on Applied Informatics: Advances in Computer Applications. Innsbruck, Austria, pp 519–524
Sun Y, Xiong Z (2006) Progressive image transmission over space-time coded OFDM-based MIMO systems with adaptive modulation. IEEE Trans Mobile Comput Arch 5(8):1016–1028
The Android platform (2010), www.google.com/android
Tzou, K. H (1987) Progressive image transmission: a review and comparison of techniques. Opt Eng. (26):581–589
Vakali A, Pallis G (2003) Content delivery networks: status and trends. IEEE Internet Comput 7(6):68–74
Wan Z, Xiong N, Ghani N, Vasilakos AV, Zhou L (2014) Adaptive unequal protection for wireless video transmission over IEEE 802.11e networks. Multimed Tools Appl 72(1):541–571
Wordnet. http://wordnet.princeton.edu/
Yang S H, Long B, Smola Alex et al Like like alike — Joint Friendship and Interest Propagation in Social Networks. WWW 2011
Zhang H, Kar K, Woods JW (2013) Performance control over heterogeneous receivers for video multicast. ICME Workshops:1–6
Zhu X, Girod B (2008) Subjective evaluation of multi-user rate allocation for streaming heterogeneous video contents over wireless networks. ICIP:3092–3095
Zhuang Y, Jiang N, Wu Z, Li Q et al (2014) Efficient and robust large medical image retrieval in mobile cloud computing environment. Inf Sci
Acknowledgments
This paper is partially supported by the Program of National Natural Science Foundation of China under Grant Nos. 61003074 and 61272188; the Program of Natural Science Foundation of Zhejiang Province under Grant Nos. LY13F020008, and LY13F020010; the Ministry of Education of Humanities and Social Sciences Project under Grant No. 14YJCZH235. The Science & Technology Innovative Team of Zhejiang Province under Grant No. 2012R10041-06.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhuang, Y., Jiang, N., Li, Q. et al. Personalized and efficient social image transmission scheme in mobile wireless network. Multimed Tools Appl 75, 2931–2968 (2016). https://doi.org/10.1007/s11042-014-2413-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-014-2413-4