On The Empirical Rate-distortion Performance Of Compressive Sensing

Compressive Sensing (CS) is a new paradigm in signal acquisition and compression. In compressive sensing, a compressible signal is acquired using much less measurements than the ones required by Nyquist theorem, provided that it is sparse in some transform domain. Recovery of the signal from the measurements is achieved by convexoptimization techniques (e.g. l1-norm minimization). CS has been attracting the interest of the signal compression community, and a vast body of knowledge about it has already been developed. When it comes to image compression applications, one is ultimately interested in how many bits one spends for a given image quality. Although several theoretical results regarding the rate-distortion performance of CS have been published recently, there are not too many practical image compression results available. The main goal of this paper is to carry out an empirical analysis of the rate distortion performance of CS in image compression. We analyze issues such as the...

• Type: Conference
• Year: 2009
• Where: ICIP
• Keywords: ICIP 2009, Image Compression, Image Compression Methods, Image Processing, Signal Compression Community