基于多层分块自适应压缩感知的图像编解码方法

摘要

In compressed sensing,the image is sampled by measurement matrix at a single sampling rate.The traditional measure-ment matrix could achieve good performance on image reconstruction,but usually occupies numerous system resources because of a large number of sampling.A multi-layered block adaptive coding algorithm (MLBA)and a multi-layered block adaptive com-pressed sensing codec method (MLBACS)are proposed.According to its regional structure,the image is divided into blocks of different layers and different sizes,which are allocated different sampling rates.Simulation results show that the sampling num-ber of MLBACS is less than traditional compressed sensing at a single sampling rate,with the same reconstruction performance.%压缩感知中，测量矩阵对图像进行单一采样率的压缩采样。传统的测量矩阵虽然能够获得比较好的重构效果，但因采样数目较多，故而资源耗费也较多。为了解决上述问题，提出了多层分块自适应编码算法（multi-layered block adaptive coding algorithm，MLBA）以及多层分块自适应压缩感知编解码方法（multi-layered block adaptive compressed sensing codec method， MLBACS）。MLBACS编解码方法基于MLBA编码算法，能够根据图像局部结构进行不同层数和大小的分块，并自适应分配采样率。仿真结果表明，在同等重构性能的前提下，相对于单一采样率下的压缩感知，MLBACS编解码方法能够不同程度地降低重构图像所需的采样数目。