An algorithm that effects fast lossless compression of multispectral-image data is based on low-complexity, proven adaptive filtering algorithms. This algorithm is intended for use in compressing multispectral-image data aboard spacecraft for transmission to Earth stations. Variants of this algorithm could be useful for lossless compression of three-dimensional medical imagery and, perhaps, for compressing image data in general.
The main adaptive-filtering algorithm on which the present algorithm is based is the sign algorithm (also known as the sign error algorithm and as the binary reinforcement algorithm). The sign algorithm is related to the least-mean-square (LMS) algorithm. Both algorithms are briefly described in the following two paragraphs.
Consider a sequence of image data (or any other data) that one seeks to compress. The sequence is specified in terms of a sequentially increasing index (k) and the value (dk) of the kth sample. An estimated value of the kth sample, k, is calculated by the equation
,
where wk is a filter-weight vector at index k and uk is an input vector that can be defined in any of a number of different ways, depending on the specific application. Once the estimate k has been calculated, the error between the estimate and the exact value is calculated as
When the LMS algorithm or sign algorithm is used as part of a predictive compression scheme, the sequence of ek values is encoded in the compressed bitstream.
The error value is also used to update the filter weights in either of two ways, depending on which algorithm is in use. In the LMS algorithm, the update equation is
In the sign algorithm, the update equation is
.
In both update equations, µ is a positive, scalar step-size parameter that controls the trade-off between convergence speed and average steady-state error. A smaller value of µ results in better steady state performance but slower convergence. In some variants of these algorithms, the value of µ changes over time.
In the present algorithm, the index k is taken as an abstract representation of three indices (x,y,z) that are the coordinates of the sample in the multispectral dataset. Specifically, x and y are the spatial coordinates and z denotes the spectral band. The signal level (equivalently, the sample value) for that location is represented by dk = s(x,y,z).
For purposes of compression, an image represented by a stream of data to be compressed is partitioned spatially into conveniently sized, fixed regions. The data are compressed in the order in which they are received, maintaining separate statistics for each band and switching among the bands as necessary. The data from each region are compressed independently of those from other regions. Performing independent compression calculations for each region limits the adverse effect of loss of data.
The input vector uk chosen for this algorithm contains values from a six sample prediction neighborhood of a sample of interest: three values from adjacent samples in the same spectral band and one sample each from the same location in each of three preceding spectral bands. Specifically, where (x,y,z) is a mean value of previous samples in the vicinity of x,y in spectral band z. The stream of ek values calculated by use of this uk is further compressed by use of Golomb codes.
In tests, the compression effectiveness of this algorithm was shown to be competitive with that of the best previously reported data-compression algorithms of similar complexity. The table presents results from one series of tests performed on multispectral imagery acquired by NASA’s airborne visible/infrared imaging spectrometer (AVIRIS).
This work was done by Matthew Klimesh of Caltech for NASA’s Jet Propulsion Laboratory.
For further information, access the Technical Support Package (TSP) free online at www.techbriefs.com/tsp under the Information Sciences category.
The software used in this innovation is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-42517.
This Brief includes a Technical Support Package (TSP).
Fast Lossless Compression Of Multispectral-Image Data
(reference NPO-42517) is currently available for download from the TSP library.
Don't have an account?
Overview
The document discusses a novel adaptive predictive technique for lossless compression of hyperspectral imagery, developed by researchers at NASA's Jet Propulsion Laboratory (JPL). The primary goal of this technique is to reduce the burden on downlink resources when transmitting hyperspectral data, which is crucial for various applications in remote sensing and imaging.
The technique employs an adaptive filtering method that achieves a balance between low complexity and effective compression, making it competitive with existing methods in the literature. The core of the algorithm is based on linear prediction, where the differences between predicted sample values and actual sample values are encoded into a compressed bitstream. This approach is a form of predictive compression, specifically differential pulse code modulation (DPCM), which relies on previously encoded samples to predict new samples, ensuring that the decoder can replicate the prediction operation.
A significant feature of the proposed compressor is its use of the sign algorithm from adaptive filtering, which enhances the accuracy of the predictions made by the compressor. The document emphasizes that while the current state of the technique represents a significant achievement, there are numerous potential avenues for further development and refinement.
The research is documented in the context of NASA's broader efforts to advance aerospace-related technologies with potential applications beyond space exploration. The findings are part of the Commercial Technology Program, which aims to disseminate aerospace innovations that can benefit various sectors.
In summary, the document presents a low-complexity, effective method for lossless compression of hyperspectral imagery, highlighting its competitive performance and adaptability. It serves as a foundation for future research and development in the field of image compression, particularly for multispectral and hyperspectral data, which are increasingly important in scientific and commercial applications. The work underscores the importance of efficient data handling in the context of modern imaging technologies and the ongoing efforts by NASA to leverage these advancements for broader technological benefits.
