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Compressive Sensing (CS) is an emerging technology which samples a sparse signal at a rate corresponding to its actual information content rather than to its bandwidth. Different from traditional coding schemes in which distortion mainly comes from quantizer, distortion are related to quantization and compressive sampling in compressive sensing based coding schemes. Since the total coding bits are...
We study the average distortion introduced by scalar, vector, and entropy coded quantization of compressive sensing (CS) measurements. The asymptotic behavior of the underlying quantization schemes is either quantified exactly or characterized via bounds. We adapt two benchmark CS reconstruction algorithms to accommodate quantization errors, and empirically demonstrate that these methods significantly...
The classical rate distortion (R-D) theories have been developed to study the relationship between the coding bit rate and signal distortion. However, with the development of the current mobile, wireless, distributed communication and the advanced audio and video coding technology, the constraints of the coding bit rate has become more and more weak while the constraints of the coding complexity has...
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