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1 July 1992 Investigative study of multispectral lossy data compression using vector quantization
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A feasibility study was conducted to investigate the advantages of data compression techniques on multispectral imagery data acquired from airborne scanners maintained and operated by NASA at the Stennis Space Center. The technique used was spectral vector quantization. The vector is defined in the multispectral imagery context as an array of pixels from the same location from each channel. The error obtained in substituting the reconstructed images for the original set is compared for different compression ratios. Also, the eigenvalues of the covariance matrix obtained from the reconstructed data set are compared with the eigenvalues of the original set. The effects of varying the size of the vector codebook on the quality of the compression and on subsequent classification are also presented. The rate of compression is programmable. However, the higher the compression ratio, the greater is the degradation between the original and the reconstructed images. The analysis for 6 channels of data acquired by the thermal infrared multispectral scanner (TIMS) resulted in compression ratios varying from 24:1 (RMS error of 8.8 pixels) to 7:1 (RMS error of 1.9 pixels). The analysis for 7 channels of data acquired by the calibrated airborne multispectral scanner (CAMS) resulted in compression ratios varying from 28:1 (RMS error of 15.2 pixels) to 8:1 (RMS error of 3.6 pixels). The technique of vector quantization can also be used to interpret the main features in the image, since those features are the ones that make up the codebook. Hence, vector quantization not only compresses the data, but also classifies it. The original and reconstructed images were not only analyzed for their RMS error but also for the similarity in their covariance matrices. Using the principal components analysis the eigenvalues of the covariance matrix of the original multispectral data-set were found to be highly correlated with those of the reconstructed data-set. The algorithms were implemented in software and interfaced with the help of dedicated image processing boards to an 80386 PC compatible computer. Modules were developed for the task of image compression and image analysis. These modules are very general in nature and are thus capable of analyzing any sets or types of images or voluminous data sets. Also, supporting software to perform image processing for visual display and interpretation of the compressed/classified images was developed.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sandeep Jaggi "Investigative study of multispectral lossy data compression using vector quantization", Proc. SPIE 1702, Hybrid Image and Signal Processing III, (1 July 1992);

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