Dept. of Electr. Eng., Polytech. Univ., Brooklyn, NY.
IEEE Trans Image Process. 1997;6(11):1555-66. doi: 10.1109/83.641415.
This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.
本文报道了一种用于多光谱图像压缩的多谱线激励线性预测（MCELP）方法。比较了不同的线性预测模型和自适应方案。已证明使用前向自适应自回归（AR）模型的方法在性能、复杂性和鲁棒性之间达到了良好的折衷。这种方法被称为 MFCELP 方法。对于一组多光谱图像，通过更新非重叠三维（3-D）宏块的线性预测系数来进行处理。每个宏块进一步划分为几个 3-D 微块，通过分析-综合过程确定每个微块的最佳激励信号。MFCELP 方法已应用于多光谱磁共振（MR）图像。为了满足医学图像的高质量要求，通过矢量量化器进一步指定原始图像集和合成图像之间的误差。该方法已应用于来自 26 项临床磁共振神经研究的图像（20 个切片/研究，3 个光谱带/切片，256x256 像素/带，12 b/像素）。与基于离散余弦变换（DCT）的联合图像专家组（JPEG）方法、基于小波变换的嵌入式零树小波（EZW）编码方法、向量树（VT）编码方法以及我们以前开发的多光谱分段自回归移动平均（MSARMA）方法相比，MFCELP 方法提供了显著的视觉改善。
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