Efficient Compressed Sensing Based Image Coding by Using Gray Transformation
This work addresses compression efficiency for image processing applications, but it appears incremental as it builds on existing CS methods with a specific preprocessing step.
The paper tackles the problem of high bit depth requirements in compressed sensing (CS) image coding by proposing a system that uses gray transformation to preprocess images, which centralizes the probability distribution of CS samples and reduces bit depth. Simulation results show the proposed system outperforms traditional CS-based coding in compression performance.
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.