Measurement coding for compressive imaging using a structural measuremnet matrix

Abstract

Compressive imaging can acquire image signal in an under-sampled (i.e., under Nyquist rate) representation called measurement. However, measurement compression still has an essential problem in its overall rate-distortion performance. In this paper, we propose a measurement prediction method in which the best predictor is directionally selected in order to reduce the entropy of measurement to be sent. Generally, the measurement prediction usually works well with a small block while the quality of recovery is known to be better with a large block. In order to overcome this dilemma, we propose to use a structural measurement matrix with which compressive sensing is done in a small block size but recovery is performed in a large block size. In this way, both prediction and recovery are expected to be improved at the same time. Experimental results show its superiority in measurement coding amounting up to bitrate reduction by 39 %.