IEEE Transactions On Circuits And Systems For Video Technology: Vol. 23, No. 12, December 2013

An Object-Oriented Visual Saliency Detection Framework Based on Sparse Coding Representations / J. Han , S. He, X. Qian, D. Wang, L. Guo, T. Liu Image Super-Resolution via Double Sparsity Regularized Manifold Learning / X. Lu, Y. Yuan, P. Yan Model and Performance of a No- Reference Quality Assessment Metric for Video Streaming / M. Seyedebrahimi, C. Bailey, X.-H. Peng Dynamic Media Assemblage / S.-J. Luo, C.-Y. Tsai, W.-C. Chen, B.-Y. Chen Embedding Invisible Codes into Normal Video Projection : Principle, Evalution, and Applications / J. Dai C. -K. R. Chung Temporally Coherent Video Saliency Using Regional Dynamic Contrast / Y. Li, B. Sheng, L. Ma, W. Wu, Z. Xie Predicting Visual Discomfort of Stereoscopic Images Using Human Attention Model / Y. J. Jung, H. Sohn, S. -I Lee, H. W. Park, Y. M. Ro A Single-Channel Architecture for Algebraic Integer-Based 8x8 2-D DCT Computation / A. Edirisuriya, A. Madanayake, R. J. Cintra, V.S. Dimitrov, and N. Rajapaksha Model Predictive Hierarchical Rate Control With Markov Decision Process for Multiview Video Coding / B. B Vizzotto, B. Zatt, M. Shafique, S. Bampi, J. Henkel Temporal Frame Interpolation Based on Multiframe Feature Trajectory / Y. -H. Cho, H. -Y. Lee, D.-S. Park Evalution of Side Information Effectiveness in Distributed Video Coding / T. Maugey, J. Gauthier, M. Cagnazzo, P. Pesquet-Popescu Perception - Inspired Background Subtraction - M. Haque, M. Murshed COMMENTS AND CORRECTIONS Corrections to "HEVC Deblocking Filter" - A. Norkin, G. Bjontegaard, A. Fuldseth, M. Narroschke, M. Ikeda, K. Andersson, M. Zhou, and G. V. d. Auwera Etc

On efficient acquisition and recovery methods for certain types of big data

Big data is characterized in many circles in terms of the three V\u27s - volume, velocity and variety. Although most of us can sense palpable opportunities presented by big data there are overwhelming challenges, at many levels, turning such data into actionable information or building entities that efficiently work together based on it. This chapter discusses ways to potentially reduce the volume and velocity aspects of certain kinds of data (with sparsity and structure), while acquiring itself. Such reduction can alleviate the challenges to some extent at all levels, especially during the storage, retrieval, communication, and analysis phases. In this chapter we will conduct a non-technical survey, bringing together ideas from some recent and current developments. We focus primarily on Compressive Sensing and sparse Fast Fourier Transform or Sparse Fourier Transform. Almost all natural signals or data streams are known to have some level of sparsity and structure that are key for these efficiencies to take place