Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery
- Created: Tuesday, 01 December 2009
The technique allows substantially smaller compressed file sizes when a small amount of distortion can be tolerated.This work extends the lossless data compression technique described in “Fast Lossless Compression of Multispectral-Image Data,” (NPO- 42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample.
A lossless predictive compression algorithm
can generally be converted to a
near-lossless compression algorithm by
quantizing the prediction residuals prior
to encoding them. In this case, since the
reconstructed sample values will not be
identical to the original sample values,
the encoder must determine the values
that will be reconstructed and use these
values for predicting later sample values.
The technique described here uses this
method, starting with the original technique,
to allow near-lossless compression.
The extension to allow near-lossless
compression adds the ability to achieve
much more compression when small
amounts of distortion are tolerable,
while retaining the low complexity and
good overall compression effectiveness
of the original algorithm.