A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Error-resilient transmission of 3D models
ACM Transactions on Graphics (TOG)
NGMAST '08 Proceedings of the 2008 The Second International Conference on Next Generation Mobile Applications, Services, and Technologies
Make3D: Learning 3D Scene Structure from a Single Still Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
M-TEEVE: real-time 3D video interaction and broadcasting framework for mobile devices
Proceedings of the 2nd International Conference on Immersive Telecommunications
Scalable and Efficient Video Coding Using 3-D Modeling
IEEE Transactions on Multimedia
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A cognitive approach for effective coding and transmission of 3D video
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Using graphics rendering contexts to enhance the real-time video coding for mobile cloud gaming
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Reliable delivery of 3D video contents to a wide set of users is expected to be the next big revolution in multimedia applications provided that it is possible to grant a certain level of Quality-of-Experience (QoE) to the end user. During the last years, several cross-layer solutions have proved to be extremely effective in tuning the transmission parameters at the different layers of the protocol stack and in maximizing the perceptual quality of the reconstructed 3D scene. Among these, Cognitive Source Coding (CSC) schemes (defined in analogy with Cognitive Radio systems) make possible to improve the quality of the 3D QoE at the receiver by adapting the source coding strategy according to the state of the transmission channel and to the characteristics of the coded signal. This knowledge also permits an optimization of the computational complexity required at the encoder. The paper presents a CSC architecture that analyzes the 3D scene, identifies the different elements, and chooses the most appropriate coding strategy via a classification of the features of each element based on Support Vector Machine theory. Experimental results show that the proposed approach permits improving the quality of the received 3D signal with respect to traditional cross-layer techniques and reducing the computational complexity of coding operation.