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Proceedings Paper

Rate-distortion optimization for compressive video sampling
Author(s): Ying Liu; Krishna Rao Vijayanagar; Joohee Kim
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Paper Abstract

The recently introduced compressed sensing (CS) framework enables low complexity video acquisition via sub- Nyquist rate sampling. In practice, the resulting CS samples are quantized and indexed by finitely many bits (bit-depth) for transmission. In applications where the bit-budget for video transmission is constrained, rate- distortion optimization (RDO) is essential for quality video reconstruction. In this work, we develop a double-level RDO scheme for compressive video sampling, where frame-level RDO is performed by adaptively allocating the fixed bit-budget per frame to each video block based on block-sparsity, and block-level RDO is performed by modelling the block reconstruction peak-signal-to-noise ratio (PSNR) as a quadratic function of quantization bit-depth. The optimal bit-depth and the number of CS samples are then obtained by setting the first derivative of the function to zero. In the experimental studies the model parameters are initialized with a small set of training data, which are then updated with local information in the model testing stage. Simulation results presented herein show that the proposed double-level RDO significantly enhances the reconstruction quality for a bit-budget constrained CS video transmission system.

Paper Details

Date Published: 23 May 2014
PDF: 7 pages
Proc. SPIE 9109, Compressive Sensing III, 91090R (23 May 2014); doi: 10.1117/12.2053407
Show Author Affiliations
Ying Liu, Illinois Institute of Technology (United States)
Krishna Rao Vijayanagar, Illinois Institute of Technology (United States)
Joohee Kim, Illinois Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9109:
Compressive Sensing III
Fauzia Ahmad, Editor(s)

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