5,787 research outputs found
Fast Lossless Compression of Multispectral-Image Data
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
An investigative study of multispectral data compression for remotely-sensed images using vector quantization and difference-mapped shift-coding
A study is conducted to investigate the effects and advantages of data compression techniques on multispectral imagery data acquired by NASA's airborne scanners at the Stennis Space Center. The first technique used was 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 output data from the Vector Quantization algorithm was further compressed by a lossless technique called Difference-mapped Shift-extended Huffman coding. The overall compression for 7 channels of data acquired by the Calibrated Airborne Multispectral Scanner (CAMS), with an RMS error of 15.8 pixels was 195:1 (0.41 bpp) and with an RMS error of 3.6 pixels was 18:1 (.447 bpp). 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. Also, supporting software to perform image processing for visual display and interpretation of the compressed/classified images was developed
Band Ordering in Lossless Compression of Multispectral Images
In this paper, we consider a model of lossless image
compression in which each band of a multispectral image is coded
using a prediction function involving values from a previously coded
band of the compression, and examine how the ordering of the bands
affects the achievable compression.
We present an efficient algorithm for computing the optimal band
ordering for a multispectral image. This algorithm has time complexity
O(n2) for an n-band image, while the naive algorithm takes time &#x03A9(n!).
A slight variant of the optimal ordering problem that is motivated by
some practical concerns is shown to be NP-hard, and hence,
computationally infeasible, in all cases except for the most trivial
possibility.
In addition, we report on our experimental findings using the
algorithms designed in this paper applied to real multispectral satellite
data. The results show that the techniques described here hold great
promise for application to real-world compression needs
Study of on-board compression of earth resources data
The current literature on image bandwidth compression was surveyed and those methods relevant to compression of multispectral imagery were selected. Typical satellite multispectral data was then analyzed statistically and the results used to select a smaller set of candidate bandwidth compression techniques particularly relevant to earth resources data. These were compared using both theoretical analysis and simulation, under various criteria of optimality such as mean square error (MSE), signal-to-noise ratio, classification accuracy, and computational complexity. By concatenating some of the most promising techniques, three multispectral data compression systems were synthesized which appear well suited to current and future NASA earth resources applications. The performance of these three recommended systems was then examined in detail by all of the above criteria. Finally, merits and deficiencies were summarized and a number of recommendations for future NASA activities in data compression proposed
Quality criteria benchmark for hyperspectral imagery
Hyperspectral data appear to be of a growing interest
over the past few years. However, applications for hyperspectral
data are still in their infancy as handling the significant size of
the data presents a challenge for the user community. Efficient
compression techniques are required, and lossy compression,
specifically, will have a role to play, provided its impact on remote
sensing applications remains insignificant. To assess the data
quality, suitable distortion measures relevant to end-user applications
are required. Quality criteria are also of a major interest
for the conception and development of new sensors to define their
requirements and specifications. This paper proposes a method to
evaluate quality criteria in the context of hyperspectral images.
The purpose is to provide quality criteria relevant to the impact
of degradations on several classification applications. Different
quality criteria are considered. Some are traditionnally used in
image and video coding and are adapted here to hyperspectral
images. Others are specific to hyperspectral data.We also propose
the adaptation of two advanced criteria in the presence of different
simulated degradations on AVIRIS hyperspectral images. Finally,
five criteria are selected to give an accurate representation of the
nature and the level of the degradation affecting hyperspectral
data
A comparison of spectral decorrelation techniques and performance evaluation metrics for a wavelet-based, multispectral data compression algorithm
Future space-based, remote sensing systems will have data transmission requirements that exceed available downlinks necessitating the use of lossy compression techniques for multispectral data. In this paper, we describe several algorithms for lossy compression of multispectral data which combine spectral decorrelation techniques with an adaptive, wavelet-based, image compression algorithm to exploit both spectral and spatial correlation. We compare the performance of several different spectral decorrelation techniques including wavelet transformation in the spectral dimension. The performance of each technique is evaluated at compression ratios ranging from 4:1 to 16:1. Performance measures used are visual examination, conventional distortion measures, and multispectral classification results. We also introduce a family of distortion metrics that are designed to quantify and predict the effect of compression artifacts on multi spectral classification of the reconstructed data
Evaluation of registration, compression and classification algorithms. Volume 1: Results
The registration, compression, and classification algorithms were selected on the basis that such a group would include most of the different and commonly used approaches. The results of the investigation indicate clearcut, cost effective choices for registering, compressing, and classifying multispectral imagery
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Predictive coding is attractive for compression onboard of spacecrafts thanks
to its low computational complexity, modest memory requirements and the ability
to accurately control quality on a pixel-by-pixel basis. Traditionally,
predictive compression focused on the lossless and near-lossless modes of
operation where the maximum error can be bounded but the rate of the compressed
image is variable. Rate control is considered a challenging problem for
predictive encoders due to the dependencies between quantization and prediction
in the feedback loop, and the lack of a signal representation that packs the
signal's energy into few coefficients. In this paper, we show that it is
possible to design a rate control scheme intended for onboard implementation.
In particular, we propose a general framework to select quantizers in each
spatial and spectral region of an image so as to achieve the desired target
rate while minimizing distortion. The rate control algorithm allows to achieve
lossy, near-lossless compression, and any in-between type of compression, e.g.,
lossy compression with a near-lossless constraint. While this framework is
independent of the specific predictor used, in order to show its performance,
in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless
compression standard, obtaining an extension that allows to perform lossless,
near-lossless and lossy compression in a single package. We show that the rate
controller has excellent performance in terms of accuracy in the output rate,
rate-distortion characteristics and is extremely competitive with respect to
state-of-the-art transform coding
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