中国邮电高校学报(英文)

中国邮电高校学报(英文) ›› 2014, Vol. 21 ›› Issue (4): 68-76.doi: 10.1016/S1005-8885(14)60318-6

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Clustering compressed sensing based on image block similarities

李卫卫1,蒋挺2,王宁1   

  1. Key Laboratory of Universal Wireless Communication, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 收稿日期:2014-02-24 修回日期:2014-06-12 出版日期:2014-08-31 发布日期:2014-08-30
  • 通讯作者: 蒋挺 E-mail:tjiang@bupt.edu.cn
  • 基金资助:

    国家自然科学基金

Clustering compressed sensing based on image block similarities

  1. Key Laboratory of Universal Wireless Communication, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2014-02-24 Revised:2014-06-12 Online:2014-08-31 Published:2014-08-30
  • Supported by:

    National Natural Science Foundation of China

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摘要： Compressed sensing (CS) algorithm enables sampling rates significantly under classical Nyquist rate without sacrificing reconstructed image quality. It is known that, a great number of images have many similar areas which are composed by the same number of grayscale or color. A new CS scheme, namely clustering compressed sensing (CCS), was proposed for image compression, and it introduces clustering algorithm onto framework of CS based on similarity of image blocks. Instead of processing the image as a whole, the image is firstly divided into small blocks, and then the clustering algorithm was proposed to cluster the similar image blocks. Afterwards, the optimal public image block in each category is selected as the representative for transmission. The discrete wavelet transform (DWT) and Gaussian random matrix are applied to each optimal public image block to obtain the random measurements. Different from equal measurements, the proposed scheme adaptively selects the number of measurements based on different sparsity of image blocks. In order to further improve the performance of the CCS algorithm, the unequal-CCS algorithm based on the characteristics of wavelet coefficients was proposed as well. The low frequency coefficients are retained to ensure the quality of reconstructed image, and the high frequency coefficients are compressed by the CCS algorithm. Experiments on images demonstrate good performances of the proposed approach.

关键词: CS, DWT, clustering algorithm

Abstract: Compressed sensing (CS) algorithm enables sampling rates significantly under classical Nyquist rate without sacrificing reconstructed image quality. It is known that, a great number of images have many similar areas which are composed by the same number of grayscale or color. A new CS scheme, namely clustering compressed sensing (CCS), was proposed for image compression, and it introduces clustering algorithm onto framework of CS based on similarity of image blocks. Instead of processing the image as a whole, the image is firstly divided into small blocks, and then the clustering algorithm was proposed to cluster the similar image blocks. Afterwards, the optimal public image block in each category is selected as the representative for transmission. The discrete wavelet transform (DWT) and Gaussian random matrix are applied to each optimal public image block to obtain the random measurements. Different from equal measurements, the proposed scheme adaptively selects the number of measurements based on different sparsity of image blocks. In order to further improve the performance of the CCS algorithm, the unequal-CCS algorithm based on the characteristics of wavelet coefficients was proposed as well. The low frequency coefficients are retained to ensure the quality of reconstructed image, and the high frequency coefficients are compressed by the CCS algorithm. Experiments on images demonstrate good performances of the proposed approach.

Key words: CS, DWT, clustering algorithm

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