J4 ›› 2015, Vol. 42 ›› Issue (4): 33-40.doi: 10.3969/j.issn.1001-2400.2015.04.006

• 研究论文 • 上一篇    下一篇



  1. (1. 西安电子科技大学 计算机学院,陕西 西安  710071;
    2. 西安石油大学 计算机学院,陕西 西安  710065)
  • 收稿日期:2014-04-16 出版日期:2015-08-20 发布日期:2015-10-12
  • 通讯作者: 梁玮
  • 作者简介:梁玮(1985-),女,西安电子科技大学博士研究生,E-mail:wwliang@mail.xidian.edu.cn.
  • 基金资助:


Multispectral image compression algorithm based on  composite transform

LIANG Wei1;ZENG Ping1,2;ZHENG Haihong1;LUO Xuemei1   

  1. (1. School of Computer Science and Technology, Xidian Univ., Xi'an  710071, China;
    2. School of Computer Science, Xi'an Shiyou Univ., Xi'an  710065, China)
  • Received:2014-04-16 Online:2015-08-20 Published:2015-10-12
  • Contact: LIANG Wei



关键词: 多光谱图像压缩, 空间稀疏等价表示, 聚类, 主成分分析, 小波编码, 误差补偿, 码率分配


Aiming at the problems of inadequate reducing of spatial and spectral redundancy and weak adaptability of existing multispectral image compression algorithms, the multispectral images' spatial sparse equivalent representation and its clustering implementation named the OptimalLeaders are proposed. Furthermore, an adaptive multispectral image compression algorithm—OLPKWS is designed, which is based on composite transform. In the OLPKWS, multispectral data are transformed into representation and residual by the presented spatial sparse equivalent transform, which removes spatial redundancy adaptively. Moreover, an error compensation mechanism is introduced in order to improve the quality of the reconstruction image. Principal Component Analysis (PCA) is used to remove spectral redundancy for representation. However, to predict differences, KLT is utilized to explore spectral correlation, two-dimensional wavelet transform is used to remove spatial redundancy, standard deviation weighted rate allocation and SPIHT are combined to complete the coding. Experimental results show that, in comparison to the clustering, SPIHT and KLT_SPIHT_TCIRA algorithms, the proposed approach achieves a higher peak signal to noise ratio (PSNR) under the same compression ratio.

Key words: multispectral image compression, spatial sparse equivalent representation, clustering, principal component analysis, wavelet coding, error compensation, rate allocation