摘要
大数据时代巨大的图像信息量,给实际的存储、传输带来了相当大的困难。有效利用图像集自身内容,去除图像之间的信息冗余,是图像集压缩的主要目的。本文提出一种基于内容自适应稀疏字典的图像集压缩方案。通过对图像内容信息进行分类学习,得到分组稀疏字典,将稀疏编码替代传统的变换编码,并利用图像非局部相似特征优化图像解码,得到更高质量的重建图像。实验结果表明,与JPEG方法以及基于递归最小二乘字典学习算法(RLS-DLA)的压缩框架相比,本方案提出的图像集压缩方法有效提高了图像集编码性能。
In the big data era,there is a huge amount of image information,which brings considerable difficulties to the actual storage,transmission,etc.The main purpose of image set compression is to make use of its own content and remove redundant information of images.In this paper,an image set compression scheme based on content adaptive sparse dictionary is proposed.A set of classification sparse dictionaries is learned by using image content classification information,and these dictionaries will be used to replace the traditional transform.In addition,this paper uses the nonlocal similarity of image patches to optimize the problem in decoder.Experimental results demonstrate that the proposed method for image set compression outperforms JPEG method and the compression scheme based on recursive least squares dictionary learning algorithm（RLS-DLA） in terms of compression property.
出处
《计算机与现代化》
2017年第11期62-66,共5页
Computer and Modernization
关键词
稀疏表示
图像集压缩
字典学习
内容自适应
图像编码
sparse representation
image set compression
dictionary learning
content adaptive
image coding
