基于压缩感知思想的图像分块压缩与重构方法

压缩感知理论在数据获取、数据存储/传输、数据分析和处理方面有很大优势,成为近年来的研究热点.考虑到大多数图像信号信息分布有差异,编码端,在对图像分块的基础上,融合熵估计和边缘检测方法计算各图像块的信息含量,再从两个不同的角度进行分类采样:依据信息量多少将图像块分为平滑、过渡和纹理3类,使用不同的采样率采样;依据信息量的分布特征,采用不同的采样率分配策略进行采样.在解码端,根据不同类型的图像块构造不同的线性算子进行重构,再运用改进的迭代阈值算法去除块效应和噪声.实验证明,算法在提升图像重构质量的同时缩短了重构时间,并且对纹理边缘多的图像的重构效果较其他方法理想.

Blocked image compression and reconstruction algorithm based on compressed sensing

Compressed sensing theory has great advantages on data acquisition, data storage and transmission, data analysis and processing. It has become a research hotspot in recent years. Considering the difference of information distribution in most image signals, at the encoding side, image entropy estimation and edge detection mechanism were used to calculate information content on the base of classification of image blocks. Then we sampled the image in two different angles： image blocks were classified according to the information content into smooth, transitional, and texture blocks, different measurement rates were used; According to the distribution characteristics of the information content, we use different sampling rate allocation strategies for sampling. At the decoding side, different linear operators were constructed to do linear reconstruction according to the different types of image blocks and further using improved iterative thresholding algorithm to remove block effect and noise. The experiments proved that the algorithm improved the quality of reconstructed image and reduced the reconstruction time at the same time and is also available for images with many textures and edges.