Binned Progressive Quantization for Compressive Sensing
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Compressive sensing (CS) has been recently and enthusiastically promoted as a joint sampling and compression approach. The advantages of CS over conventional signal compression techniques are architectural: the CS encoder is made signal independent and computationally inexpensive by shifting the bulk of system complexity to the decoder. While these properties of CS allow signal acquisition and communication in some severely resource-deprived conditions that render conventional sampling and coding impossible, they are accompanied by rather disappointing rate-distortion performance. In this paper, we propose a novel coding technique that rectifies, to a certain extent, the problem of poor compression performance of CS and, at the same time, maintains the simplicity and universality of the current CS encoder design. The main innovation is a scheme of progressive fixed-rate scalar quantization with binning that enables the CS decoder to exploit hidden correlations between CS measurements, which was overlooked in the existing literature. Experimental results are presented to demonstrate the efficacy of the new CS coding technique. Encouragingly, on some test images, the new CS technique matches or even slightly outperforms JPEG.
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