Lincoln Laboratory

Massachusetts Institute of Technology

Lexington, Massachusetts

Lossless compression takes advantage of redundancy in the scene and through efficient coding allows the reconstruction of the original data exactly. The reduction ratios in data quantity achievable are approximately 1.5 to 2 for single band images, but can be as high as 3 or 4 for multiband images when band-to-band correlation is present [2]. One technique involves the use of predictive coding of image pixels based on neighbors (spatial and/or spectral) and a model of the differences, followed by entropy encoding/decoding of the predictive coefficients using Huffman, Rice or Arithmetic codes. Compression occurs in these techniques by developing a "codebook" which assigns codes with short bit length to frequently appearing image grayscale levels and longer code words to infrequent levels. Thus, images with high spatial uniformity (small differences from pixel to pixel) can be highly compressed with no loss of information. However, these schemes can become sensitive to bit errors in the communications link and require a certain amount of overhead to ensure no loss of data.

Lossy compression techniques allow one to tradeoff compression ratio with image quality. Usable lossy compression ratio's range from 4 or 5 with minimal image degradation to 40 or 50 with image quality that may still be adequate for qualitative interpretation. There are several approaches to data reduction [3]. Scalar or vector quantizers use discrete codebooks similar to lossless compression techniques but compression is achieved by limiting the number of possible codes. Model-based approaches such as Fractal techniques represent the data with an algorithm and a minimal number of coefficients and work best with images that have self-similarity at several scales. Transform techniques achieve compression by truncating and quantizing the number of coefficients and basis functions that are used to represent the image. Several studies have shown that for multispectral images a Karhunen-Loeve (or Principle Components) Transform (KLT) to spectrally decorrelate the bands followed by a 2-D Discrete Cosine (DCT) or Wavelet Transform to spatially compress the imagery achieve the lowest information degradation according to several metrics among lossy compression techniques. However, the KLT requires the computation of the eigenvectors of the spectral covariance matrix of the scene, and unless these can be precomputed, this burden may make this approach unfeasible for many applications. For single band images the DCT, which forms the basis for the popular JPEG image compression technique, appears to be the best choice for many applications.

It is important to emphasize that the choice of compression technique and ratio, and the resulting impact on the data, is very dependent upon the system characteristics. Features in the scene, atmospheric effects, sensor noise, scanning vs. staring sensors, spectral band misregistration, spectrally varying spatial response and the ultimate application all can affect compression issues. Also, most compression schemes require that statistical or other model parameters be computed from the data, or at least from similar data, and the speed of computation, accuracy and robustness of these parameters are important considerations.

[2] Tate, S.R., "Band Ordering in Lossless Compression of Multispectral Images," Proceedings of the 1994 Data Compression Conference, IEEE Computer Society Press, pp. 311-320.

[3] Conference Record of The 27th Asilomar Conference on Signals, Systems & Computers, edited by A. Singh, Vol. 2, IEEE Computer Society Press, November, 1993.

[4] Lurie, J.B, B.W. Evans, B. Ringer, M. Yeates, "Image Quality Measures to Assess Hyperspectral Compression Techniques," Proceedings of Conference on Microwave Instrumentation and Satellite Photogrammetry for Remote Sensing of the Earth, SPIE Vol. 2313, 1994, pp. 2-14.

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