基于解析字典的图像压缩方法

Image Compression Algorithm Based on Analysis Dictionary

随着高分辨率图像采集技术的发展,采集图像的数据量大幅增加给图像的压缩、存储和传输带来了严重的挑战。近年来,压缩感知理论以能够通过少量观测信号高精度重建原始信号的优势给图像压缩带来了新的思路。目前压缩感知理论的研究主要集中于综合模型,然而综合模型下图像数据的稀疏表示却存在着计算量大、耗时长的问题。提出了基于解析字典的图像压缩模型。在分析综合模型字典学习方法的基础上介绍了解析模型字典学习的流程,然后详细分析了基于解析字典的图像压缩的总流程。以标准测试库自然图像为数据对模型的重构质量和系统耗时进行了分析,结果表明,本文所提模型在采样率相同的情况下,相对其他模型不仅能够缩短系统的耗时,也提高了图像重构的准确性。

With the development of high resolution imaging techniques,the volume of data adds serious challenges to the compression,storage and transmission of images.Recently,compressed sensing theory adds new solutions to image compression since the ability of reconstructing original signals with a small amount of observation values.The sparsity of the signal is the premise of the application of the compressed sensing theory,so the sparse representation of the data is the key step in the compression of image.The key of sparse representation is the dictionary,and the main dictionary models are synthesis model and analysis model.Along with the extension of the application of the dictionary learned through the synthesis model in the image compression,the time-consuming of the image in the sparse representation becomes a key factor restricting the efficiency of the system.Therefore,in view of the defect of the synthesis model in the application,combined with the advantages of the analysis model in the process of sparse representation,we proposed an image block compression model based on analysis dictionary（ALDBCS）.In this model,firstly,a dictionary is learned through the prior data set,and then in order to reduce the cost of the sparse representation,the dictionary is introduced to the process of image compression.The standard testing library of natural images is used as testing images,time-consuming and reconstruction quality are taken as evaluation criterions,the experimental results proves that the ALDBCS model can not only improve the quality of image reconstruction,but also reduce the time consuming of image compression.