Multispectral code excited linear prediction coding and its application in magnetic resonance images

Jian Hong Hu, Yao Wang, Patrick T. Cahill

Research output: Contribution to journalArticlepeer-review


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 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, 256 x 256 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.

Original languageEnglish (US)
Pages (from-to)1555-1566
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number11
StatePublished - 1997


  • CELP coding
  • Medical image compression
  • Multispectral image compression

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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