侯兴松1, 韩敏1, 龚晨2(1.西安交通大学电子与信息工程学院, 西安 710049;2.高通公司圣地亚哥研究院, 加利福尼亚州, 美国)
目的 CCSDS-IDC（国际空间数据系统咨询委员会-图像数据压缩）是NASA制定的基于离散小波变换（DWT）尺度间衰减性的空间图像数据压缩标准，适用于合成孔径雷达（SAR）幅度图像及各类遥感图像的压缩。然而，与光学图像不同，常见的SAR图像都是复图像数据，其在干涉测高等许多场合具有广泛应用，分析研究CCSDS-IDC对SAR复图像数据的编码性能具有重要的应用价值。方法 由于SAR复图像数据不具有尺度间的衰减性，因此将其用于SAR复图像数据编码时性能较低。考虑到SAR复图像数据离散小波变换（DWT）系数呈现出聚类特性，提出将四叉树（QC）用于DWT域的SAR复图像数据编码，发现QC对SAR复图像数据具有高效的压缩性能。结果 实验结果表明，在同等码率下，对基于DWT的SAR复图像数据压缩，QC比CCSDS-IDC最多可提高幅度峰值信噪比4.4 dB，平均相位误差最多可降低0.368；与基于方向提升小波变换（DLWT）的CCSDS-IDC相比，QC可提高峰值信噪比3.08 dB，降低平均相位误差0.25；对其他类型的图像压缩，基于聚类的QC仍能获得很好的编码性能。结论 CCSDS-IDC对SAR复图像数据编码性能低下，而QC能获得很好的编码性能。对应于图像平滑分布的尺度间衰减性，其在某些特殊图像中可能不存在，而对应于图像结构分布的聚类特性总是存在的，故在基于DWT的图像编码算法设计中，应优先考虑利用小波系数的聚类特性，从而实现对更多种类图像的高效编码。
Performance analysis of CCSDS-IDC for SAR complex image data and quadtree-based SAR complex image data coding
Hou Xingsong1, Han Min1, Gong Chen2(1.School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China;2.Qualcomm Research San Diego, CA., USA)
Objective CCSDS-IDC(consultative committee for space data systems-image data compression),which has been proposed as a space remote image data compression standard by NASA,is suitable for synthetic aperture radar(SAR) amplitude image and other remote sensing image compression. It uses discrete wavelet transform(DWT),and performs hierarchical DWT coefficients encoding from large scales to small scales in the bit plane encoding. When the energy of DWT coefficients mainly concentrates on the large scale(low frequency) and there is energy attenuation from largescale to small scale,CCSDS-IDC can achieve high compression efficiency. Unlike optical images,SAR image data is always complex-valued,which has a wide range of use. For example,interferometric SAR can use the phase difference of two complex SAR images to obtain elevation information and has been widely applied in environmental monitoring,mapping,and other fields. However,vast amounts of complex SAR image data require transmission and storage resources,which raise the needs for efficient SAR complex image data compression. Method In this paper,we study the performance of CCSDS-IDC for complex SAR image data and identify that CCSDS-IDC suffers from low efficiency for complex SAR image data coding. As the real part and imaginary part of SAR complex image data have many high-frequency oscillation components,the large-amplitude significant DWT coefficients mainly concentrate on a small scale and the DWT coefficients do not exhibit attenuation from large scale to small scale. Due to this,CCSDS-IDC has to spend a large amount of bits on coding the significant DWT coefficients on a small scale for the complex SAR image data,which degrades the rate distortion performance of CCSDS-IDC. Considering the clustering characteristics of DWT coefficients corresponding to the spatial structure of the SAR complex image data,we propose Quadtree Coding(QC) based on DWT for complex SAR image data compression,and find that QC achieves high performance for complex SAR image data. Result Compared with CCSDS-IDC based on DWT,QC based on DWT can improve the amplitude peak-signal-to-noise-ratio(PSNR) up to 4.4 dB and reduce the MPE up to 0.368 at the same bitrate. Though the directional lifting wavelet transform(DLWT) can aggregate the energy of wavelet coefficients to low frequency and improve the performance of CCSDS-IDC,the QC based on DWT still outperform the CCSDS-IDC based on DLWT for complex SAR image data compression. Compared with CCSDS-IDC based on DLWT,the QC based on DWT can improve the amplitude PSNR up to 3.08 dB and reduce the MPE 0.25. For other images,such as SAR amplitude images and optical images,which exhibit different DWT coefficients properties from that of complex SAR image data,QC based on DWT also achieves nice compression performance. Conclusion CCSDS-IDC has a poor performance for complex SAR image data,however,the QC is more suitable for complex SAR image data coding. For designing image coding algorithm based on DWT,compared with using the attenuation property,exploiting the clustering property may be the first choice to code more kinds of images efficiently. This is because clustering characteristic being related to the image geometrical structures always exist while attenuation characteristic being related to some smooth constrains may lack for some kinds of images.