Adaptive emergency scenery video communications using HEVC for responsive decision support in disaster incidents

This study proposes a unifying framework for m-Health video communication systems that provides for the joint optimization of video quality, bitrate demands, and encoding time. The framework is video modality and infrastructure independent and facilitates adaptation to the best available encoding mode that satisfies underlying technology and application imposed constraints. The scalability of the proposed algorithm is demonstrated using different HEVC encoding configurations and realistic modelling of 802.11× wireless infrastructure for emergency scenery and response videos. Extensive experimentation shows that a jointly optimal solution in the encoding time, bitrate, and video quality space is feasible.

[1]  A. Panayides,et al.  Open-Source Telemedicine Platform for Wireless Medical Video Communication , 2013, International journal of telemedicine and applications.

[2]  Matteo Mazzotti,et al.  Robust Multilayer Control for Enhanced Wireless Telemedical Video Streaming , 2010, IEEE Transactions on Mobile Computing.

[3]  Yuebing Jiang,et al.  A dynamically reconfigurable architecture system for time-varying image constraints (DRASTIC) for motion JPEG , 2014, Journal of Real-Time Image Processing.

[4]  Marios S. Pattichis,et al.  Atherosclerotic Plaque Ultrasound Video Encoding, Wireless Transmission, and Quality Assessment Using H.264 , 2011, IEEE Transactions on Information Technology in Biomedicine.

[5]  Stavros Stavrou,et al.  Review of constitutive parameters of building materials , 2003 .

[6]  Yuebing Jiang,et al.  Dynamically reconfigurable DCT architectures based on bitrate, power, and image quality considerations , 2012, 2012 19th IEEE International Conference on Image Processing.

[7]  Marios S. Pattichis,et al.  M-health medical video communication systems: An overview of design approaches and recent advances , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Ming Yang,et al.  Hybrid ray-tracing model for radio wave propagation through periodic building structures , 2011 .

[9]  Marios S. Pattichis,et al.  High-Resolution, Low-Delay, and Error-Resilient Medical Ultrasound Video Communication Using H.264/AVC Over Mobile WiMAX Networks , 2013, IEEE Journal of Biomedical and Health Informatics.

[10]  Marios S. Pattichis,et al.  Adaptive real-time HEVC encoding of emergency scenery video , 2014, 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH).

[11]  Marios S. Pattichis,et al.  Abdominal Aortic Aneurysm medical video transmission , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[12]  Marios S. Pattichis,et al.  Mobile-Health Systems Use Diagnostically Driven Medical Video Technologies [Life Sciences] , 2013, IEEE Signal Processing Magazine.

[13]  M.S. Pattichis,et al.  m-Health e-Emergency Systems: Current Status and Future Directions [Wireless corner] , 2007, IEEE Antennas and Propagation Magazine.

[14]  Álvaro Alesanco Iglesias,et al.  Enhanced Protocol for Real-Time Transmission of Echocardiograms Over Wireless Channels , 2012, IEEE Transactions on Biomedical Engineering.

[15]  Ali Alinejad Cross-Layer Ultrasound Video Streaming Over Mobile WiMAX and HSUPA Networks , 2012, IEEE Transactions on Information Technology in Biomedicine.

[16]  Marios S. Pattichis,et al.  rn-Health le-Emergency Systems: Current Status and Future Directions , 2006 .

[17]  Nada Y. Philip,et al.  Medical QoS provision based on reinforcement learning in ultrasound streaming over 3.5G wireless systems , 2009, IEEE Journal on Selected Areas in Communications.

[18]  David Flynn,et al.  HEVC Complexity and Implementation Analysis , 2012, IEEE Transactions on Circuits and Systems for Video Technology.