摘要
针对传统深空图像编码导致系统资源极大浪费的问题,提出了基于sym8小波和部分hadmard矩阵的深空图像压缩感知编码方法。对图像sym8小波分解后的低频系数进行3级小波分解后用CCSDS编码,高频系数利用部分hadmard矩阵观测后进行量化编码。解码时,CCSDS解码恢复低频系数,正交匹配追踪算法恢复高频系数,然后再通过逆sym8小波变换合成图像。仿真结果表明:相同压缩比下峰值信噪比比小波稀疏基方法和单层小波压缩感知方法分别有5～8 dB和1～1.4 dB的明显提升。
In view of the problem of classical deep-space image coding resulting in great waste of system resource,the authors proposed a deep-space image coding method based on the sym8 wavelet bases and the partial hadmard matrix.After the sym8 wavelet decomposition of images,the low frequency coefficients were conducted by the three-layer wavelet decomposition and then CCSDS encoding;the high frequency coefficients were observed by use of the part hadmard matrix and then were conducted by quantify coding.For the decoding,the CCSDS decoding was used to recover the low frequency coefficients and the orthogonal matching pursuit algorithm was used to restore the high frequency coefficients,then the images were reconstructed through the inverse sym8 wavelet transformation.The simulation results show： at the same compression ratio,compared with the wavelet sparse base method and the single-layer wavelet compressed sensing method,the peak value signal-to-noise ratio of the proposed method has 5~8 dB and 1~1.4 dB average improvements respectively.
出处
《重庆邮电大学学报（自然科学版）》
北大核心
2012年第5期646-651,共6页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
国家自然科学基金资助项目(61271261
61102151
60972069
61001105)
重庆市科委自然科学基金(CSTC2012jjA40048
CSTC 2011BA2041)~~
