Ms. Ying Liu Profile

Ying Liu

at Illinois Institute of Technology

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Proceedings Article | 23 May 2014 Paper

Rate-distortion optimization for compressive video sampling

Ying Liu, Krishna Vijayanagar, Joohee Kim

Proceedings Volume 9109, 91090R (2014) https://doi.org/10.1117/12.2053407

KEYWORDS: Video, Quantization, Video compression, Data modeling, Compressed sensing, Computer programming, Video coding, Reconstruction algorithms, Promethium, Distortion

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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.

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