On the Empirical Rate-Distortion Performance
Presented at IEEE International Conference on Image Processing (ICIP), November 2009.
Compressive Sensing (CS) is a new paradigm in signal acquisition and compression that has been attracting the interest of the signal compression community. When it comes to image compression applications, it is relevant to estimate the number of bits required to reach a specific image quality. Although several theoretical results regarding the rate-distortion performance of CS have been published recently, there are not 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 minimization algorithm used and the transform employed, as well as the trade-off between number of measurements and quantization error. From the experimental results obtained we highlight the potential and limitations of CS when compared to traditional image compression methods.
Extended version of the results:
This [.zip] archive contains all
MATLAB functions used to generate the results, including the optimization
L1-Magic and the algorithm for generating Noiselets made available by
Justin Romberg. CS recovery strategies that make use of Wavelets