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.
翻译：近年来,基于压缩的图像编码已成为图像处理领域的一个热题。然而,由于对每个 CS 样本进行编码所需的比重深度太大,这一模式的压缩性能是没有吸引力的。为了解决这一问题,提议了一个新的 CS 图像编码系统,采用灰色转换法。在拟议的系统中,我们首先使用灰色转换法来预处理原始图像,然后使用 CS 来抽样已变图像。由于灰色转换法使 CS 样本的概率分布集中,因此对每个 CS 样本进行编码所需的比重深度大大降低。因此,拟议的系统可以大大改善 CS 图像编码的压缩性能。模拟结果显示,拟议的系统在压缩性能方面不使用灰色转换法,比传统系统更优。
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