Abstract
We present an end-to-end image compression system based on compressive sensing. The presented system integrates the conventional scheme of compressive sampling and reconstruction with quantization and entropy coding. The compression performance, in terms of decoded image quality versus data rate, is shown to be comparable with JPEG and significantly better at the low rate range. We study the parameters that influence the system performance, including (i) the choice of sensing matrix, (ii) the trade-off between quantization and compression ratio, and (iii) the reconstruction algorithms. We propose an effective method to jointly control the quantization step and compression ratio in order to achieve near optimal quality at any given bit rate. Furthermore, our proposed image compression system can be directly used in the compressive sensing camera, e.g. the single pixel camera, to construct a hardware compressive sampling system.
Abstract (translated)
我们提出了一种基于压缩感知的端到端图像压缩系统。所提出的系统将传统的压缩采样和重建方案与量化和熵编码相结合。就解码图像质量与数据速率而言，压缩性能显示为与JPEG相当，并且在低速率范围内明显更好。我们研究影响系统性能的参数，包括（i）传感矩阵的选择，（ii）量化和压缩比之间的权衡，以及（iii）重建算法。我们提出了一种有效的方法来联合控制量化步长和压缩比，以便在任何给定的比特率下实现接近最佳的质量。此外，我们提出的图像压缩系统可以直接用于压缩感测相机，例如，单像素摄像头，构建硬件压缩采样系统。
URL
https://arxiv.org/abs/1706.01000
