Cooperative content and radio resource allocation for visual information maximization in a digital signage scenario

In this paper, we present a cooperative content and resource allocation algorithm that selects networks and sub-carriers for digital signage scenarios based on visual information. In these scenarios, both 2D and 3D content are handled in open space for the advertisement of commercial products. To quantify visual information, we propose a quality of visual service (QoVS) metric based on human perception. We then construct the expected QoVS problem to guarantee the maximum QoVS for service users. The QoVS is determined based on the level of 2D visual sensitivity, and on the ability to perform 3D binocular fusion by users located at various viewing distances. By utilizing the QoVS, we predict wireless packet errors and loss of visual information caused by limited radio resources. After 3D content is selected to be multicasted to users by means of the large displays, sub-carriers are optimally allocated for the remaining smartphone users to facilitate point-to-point communication through lossy wireless channels. Simulation results of the proposed scheme demonstrate the advantages of automatic control of visual information and radio resources for multiple users without additional interactions. Moreover, the method developed herein can be flexibly applied with low complexity to several visual application services provided over heterogeneous displays and channels, such as advertisements, exhibitions, and forums.

[1]  D. Marpe,et al.  The H.264/MPEG4 advanced video coding standard and its applications , 2006, IEEE Communications Magazine.

[2]  Jianhua Lu,et al.  M-PSK and M-QAM BER computation using signal-space concepts , 1999, IEEE Trans. Commun..

[3]  Hideyuki Tamura,et al.  Gaze-directed adaptive rendering for interacting with virtual space , 1996, Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium.

[4]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[5]  Wa Wijnand IJsselsteijn,et al.  Visual discomfort in stereoscopic displays: a review , 2007, Electronic Imaging.

[6]  Sanghoon Lee,et al.  Visual Entropy Gain for Wavelet Image Coding , 2006, IEEE Signal Processing Letters.

[7]  Sungjin Lee,et al.  Optimization of Delay-Constrained Video Transmission for Ad Hoc Surveillance , 2014, IEEE Transactions on Vehicular Technology.

[8]  Y. Narahari,et al.  Nonconvex piecewise linear knapsack problems , 2009, Eur. J. Oper. Res..

[9]  Weixiong Zhang Branch-and-Bound Search Algorithms and Their Computational Complexity. , 1996 .

[10]  Jimmy Schaeffler,et al.  Digital Signage: Software, Networks, Advertising, and Displays: A Primer for Understanding the Business , 2008 .

[11]  Alan C. Bovik,et al.  3D Visual Activity Assessment Based on Natural Scene Statistics , 2014, IEEE Transactions on Image Processing.

[12]  Marios S. Pattichis,et al.  Foveated video compression with optimal rate control , 2001, IEEE Trans. Image Process..

[13]  Lars-Ingemar Lundstrom Digital Signage Broadcasting : Content Management and Distribution Techniques , 2013 .

[14]  Masaki Emoto,et al.  Research on Human Factors in Ultrahigh-Definition Television (UHDTV) to Determine its Specifications , 2008 .

[15]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[16]  Alan C. Bovik,et al.  Optimal Channel Adaptation of Scalable Video Over a Multicarrier-Based Multicell Environment , 2009, IEEE Transactions on Multimedia.

[17]  Ahmet M. Kondoz,et al.  Quality analysis for 3D video using 2D video quality models , 2008, IEEE Transactions on Consumer Electronics.

[18]  Uk Jang,et al.  Optimal Carrier Loading Control for the Enhancement of Visual Quality over OFDMA Cellular Networks , 2008, IEEE Transactions on Multimedia.

[19]  Jerzy Grobelny,et al.  Various approaches to a human preference analysis in a digital signage display design , 2011, ArXiv.

[20]  Alan C. Bovik,et al.  Saliency Prediction on Stereoscopic Videos , 2014, IEEE Transactions on Image Processing.

[21]  David J. Sakrison,et al.  The effects of a visual fidelity criterion of the encoding of images , 1974, IEEE Trans. Inf. Theory.

[22]  René Beier,et al.  Random knapsack in expected polynomial time , 2003, STOC '03.

[23]  Alan C. Bovik,et al.  Image information and visual quality , 2006, IEEE Trans. Image Process..

[24]  John D. Villasenor,et al.  Visibility of wavelet quantization noise , 1997, IEEE Transactions on Image Processing.

[25]  J. Robson,et al.  Application of fourier analysis to the visibility of gratings , 1968, The Journal of physiology.

[26]  Bala Shetty,et al.  The nonlinear knapsack problem - algorithms and applications , 2002, Eur. J. Oper. Res..

[27]  Sanghoon Lee,et al.  Device-Aware Visual Quality Adaptation for Wireless N-Screen Multicast Systems , 2013, IEICE Trans. Commun..

[28]  Matti Latva-aho,et al.  Weighted sum-rate maximization for a set of interfering links via branch and bound , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[29]  Mei Yu,et al.  Binocular energy response based quality assessment of stereoscopic images , 2014, Digit. Signal Process..

[30]  Kenichiro Masaoka,et al.  UHDTV Image Format for Better Visual Experience , 2013, Proceedings of the IEEE.

[31]  Dorit S. Hochbaum,et al.  A nonlinear Knapsack problem , 1995, Oper. Res. Lett..

[32]  Bala Shetty,et al.  A pegging algorithm for the nonlinear resource allocation problem , 2002, Comput. Oper. Res..

[33]  Michael J. Black,et al.  Secrets of optical flow estimation and their principles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.