  In recent years, compressed sensing (CS) based image coding has become a hot
topic in image processing field. However, since the bit depth required for
encoding each CS sample is too large, the compression performance of this
paradigm is unattractive. To address this issue, a novel CS-based image coding
system by using gray transformation is proposed. In the proposed system, we use
a gray transformation to preprocess the original image firstly and then use CS
to sample the transformed image. Since gray transformation makes the
probability distribution of CS samples centralized, the bit depth required for
encoding each CS sample is reduced significantly. Consequently, the proposed
system can considerably improve the compression performance of CS-based image
coding. Simulation results show that the proposed system outperforms the
traditional one without using gray transformation in terms of compression
performance.
