Image Compression Algorithm Based on Analysis Dictionary

FENG Zongwei; ZHONG Yanwei; ZHENG Weiling; PAN Shaoming

doi:10.13203/j.whugis20160085

Volume 43 Issue 2

Feb. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(2): 262-267, 274. > DOI: 10.13203/j.whugis20160085

FENG Zongwei, ZHONG Yanwei, ZHENG Weiling, PAN Shaoming. Image Compression Algorithm Based on Analysis Dictionary[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 262-267, 274. DOI: 10.13203/j.whugis20160085

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Image Compression Algorithm Based on Analysis Dictionary

1.
Stale Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sersing, Wuhan University, Wuhan 430079, China

Funds:

The National Natural Science Foundation of China 61572372

The National Natural Science Foundation of China 41671382

The National Natural Science Foundation of China 41271398

the Fund of SAST SAST201425

LIESMARS Special Research Funding

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Author Bio:
FENG Zongwei, master, specializes in computer vision and image processing. E-mail: fengzongwei@yeah.net
Corresponding author:
CHONG Yanwen, PhD, professor. E-mail: ywchong@whu.edu.cn
Received Date: November 15, 2016
Published Date: February 04, 2018

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Abstract

Abstract

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.
- blocking compression model,
- analysis model,
- analysis dictionary,
- image reconstruction,
- learning dictionary offline

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