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Personalized and efficient social image transmission scheme in mobile wireless network

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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.

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Notes

  1. Available at http://www.flickr.com

  2. Available at http://www.youtube.com

  3. 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.

  4. Strictly speaking, the pixel resolutions of the three ROIs in Fig. 8b are different based on the user preferences.

References

  1. 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

    Article  Google Scholar 

  2. Aziz SM, Pham DM (June) Energy efficient image transmission in wireless multimedia sensor networks. IEEE Commun Lett 17(6):1084–1087

  3. Buyya R, Pathan M, Vakali A (2008) Content delivery networks. Springer

  4. Chang RC, Shih TK, Hsu HH (2008) A strategic decomposition for adaptive image transmission. J Inf Sci Eng 24(3):691–707

    Google Scholar 

  5. 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

  6. 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

    Google Scholar 

  7. 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

  8. Charles JT, Larry LP (1992) Image transfer: an end-to-end design. In ACM SIGCOMM International Conference on Data Communication, 258–268

  9. 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

  10. 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

    Article  Google Scholar 

  11. John MD, Georey MD, Song XY (1995) Fast lossy internet image transmission. In ACM Int’l Conference on Multimedia

  12. 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

  13. Li X, Snoek C, Worring M, Smeulders A (2012) Harvesting social images for biconcept search. IEEE Trans Multimed 14(4):1091–1104

    Article  Google Scholar 

  14. Li X, Snoek C, Worring M (2009) Learning social tag relevance by neighbor voting. IEEE Trans Multimed 11(7):1310–1322

    Article  Google Scholar 

  15. Lin T, Hao P (2005) Compound image compression for real-time computer screen image transmission. IEEE Trans Image Process 14(8):993–1005

    Article  MathSciNet  Google Scholar 

  16. MySQL (2010). http://www.mysql.com/

  17. Panoramio (2009). http://www.panoramio.com/

  18. Paul S, Fei Z (2001) Distributed caching with centralized control. Comput Commun 24(2):256–268

    Article  Google Scholar 

  19. 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

    Google Scholar 

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. Rabbat R (2010) Web P a new image format for the Web. Chromium Blog. Google. Retrieved. 10-01

  23. Raman S, Balakrishnan H, Srinivasan M (2000) An image transport protocol for the Internet. In International Conference on Network Protocol, 209–219

  24. 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

  25. 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

    Article  Google Scholar 

  26. The Android platform (2010), www.google.com/android

  27. Tzou, K. H (1987) Progressive image transmission: a review and comparison of techniques. Opt Eng. (26):581–589

  28. Vakali A, Pallis G (2003) Content delivery networks: status and trends. IEEE Internet Comput 7(6):68–74

    Article  Google Scholar 

  29. 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

    Article  Google Scholar 

  30. Wordnet. http://wordnet.princeton.edu/

  31. Yang S H, Long B, Smola Alex et al Like like alike — Joint Friendship and Interest Propagation in Social Networks. WWW 2011

  32. Zhang H, Kar K, Woods JW (2013) Performance control over heterogeneous receivers for video multicast. ICME Workshops:1–6

  33. Zhu X, Girod B (2008) Subjective evaluation of multi-user rate allocation for streaming heterogeneous video contents over wireless networks. ICIP:3092–3095

  34. 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

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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.

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Authors and Affiliations

  1. College of Computer & Information Engineering, Zhejiang Gongshang University, Hangzhou, People’s Republic of China

    Yi Zhuang

  2. Hangzhou First People’s Hospital, Hangzhou, People’s Republic of China

    Nan Jiang

  3. Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, People’s Republic China

    Qing Li

  4. Faculty of Education, The University of Hong Kong, Pok Fu Lam, HKSAR, People’s Republic of China

    Dickson K. W. Chiu

  5. School of Computer, Hangzhou Dianzi University, Hangzhou, People’s Republic of China

    Hua Hu

  6. Apart.502, Unit 2, Blg.7, Qinchunfang, Hangzhou, People’s Republic of China

    Yi Zhuang

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  1. Yi Zhuang
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  2. Nan Jiang
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  3. Qing Li
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  4. Dickson K. W. Chiu
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  5. Hua Hu
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Correspondence to Yi Zhuang.

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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

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  • DOI: https://doi.org/10.1007/s11042-014-2413-4

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