scholarly journals Optimal Compression of High Spectral Resolution Satellite Data via Adaptive Vector Quantization with Linear Prediction

2008 ◽  
Vol 25 (6) ◽  
pp. 1041-1047 ◽  
Author(s):  
Bormin Huang ◽  
Alok Ahuja ◽  
Hung-Lung Huang
Keyword(s):  
Data Compression ◽  
Vector Quantization ◽  
Satellite Data ◽  
Spectral Resolution ◽  
Image Coding ◽  
Linear Prediction ◽  
Lossless Compression ◽  
High Spectral Resolution ◽  
Adaptive Vector Quantization ◽  
Space Data

Abstract Contemporary and future high spectral resolution sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. In this paper, a novel adaptive vector quantization (VQ)-based linear prediction (AVQLP) method for lossless compression of high spectral resolution sounder data is proposed. The AVQLP method optimally adjusts the quantization codebook sizes to yield the maximum compression on prediction residuals and side information. The method outperforms the state-of-the-art compression methods [Joint Photographic Experts Group (JPEG)-LS, JPEG2000 Parts 1 and 2, Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) 5/3, Context-Based Adaptive Lossless Image Coding (CALIC), and 3D Set Partitioning in Hierarchical Trees (SPIHT)] and achieves a new high in lossless compression for the standard test set of 10 NASA Atmospheric Infrared Sounder (AIRS) granules. It also compares favorably in terms of computational efficiency and compression gain to recently reported adaptive clustering methods for lossless compression of high spectral resolution data. Given its superior compression performance, the AVQLP method is well suited to ground operation of high spectral resolution satellite data compression for rebroadcast and archiving purposes.

Mercury's Na exosphere as seen with very high spectral resolution from the ground, and from space with MESSENGER

2020 ◽  
Author(s):  
Nelly Mouawad ◽  
Judy Chebly ◽  
François Leblanc ◽  
Jonathan Fraine ◽  
Kahil Fatima
Keyword(s):  
Spectral Resolution ◽  
Doppler Shift ◽  
Space Environment ◽  
High Spectral Resolution ◽  
Polar Regions ◽  
University Of Texas ◽  
Intensity Measurements ◽  
Trajectory Approach ◽  
Space Data ◽  
Very High

<p>The MErcury Surface, Space ENvironment, GEochemistry, and Ranging NASA’s spacecraft, known as MESSENGER, flew by Mercury on September 29, 2009. It was the spacecraft’s third and final flyby, before it went into orbit around the planet. The flyby presented a unique trajectory approach and perspective on the planet’s exosphere, not available when in orbit. We present very high spectral resolution ground-based data obtained at the University of Texas McDonald 2.7-m telescope. These data were acquired within hours of the data taken with the Ultraviolet and Visible Spectrometer (UVVS) onboard MESSENGER. Both datasets targeted similar spatial regions, in the polar altitudes of Mercury. We compare the sodium emissions from both measurements in the exosphere. We find that close to the surface, both intensity measurements match, but the intensities fall off differently with altitude, with the MESSENGER data showing an exponential drop off, sharper than that of the ground-based data; an effect that we attribute to atmospheric seeing. In addition, our ground-based data provided Full Width Half Maximum (fwhm) speeds and Doppler shift speeds; our results suggest energetic processes took place in the polar regions on the dusk side of the planet, but arguably on the dawn side as well. We confirm previous conclusions of Leblanc et al. (2008, 2009) where signatures of energetic processes seem to be coupled with high fwhm speeds and intensity peaks. We compare our Doppler shift velocities with previous works, and find agreement within the uncertainties with Potter et al., (2013) on their transit velocity measurements. Although our peak emissions along the terminator vary in structure and in brightness, they do not exhibit distinctive signatures in the intensity profiles at altitudes above the poles, when compared with convolved MESSENGER space data.</p>

Adaptive vector quantization with fuzzy distortion measure for image coding

1996 ◽  
Author(s):  
Suryalakshmi Pemmaraju ◽  
Sunanda Mitra ◽  
L. Rodney Long ◽  
George R. Thoma ◽  
Yao-Yang Shieh ◽  
...  
Keyword(s):  
Vector Quantization ◽  
Image Coding ◽  
Adaptive Vector Quantization ◽  
Distortion Measure

Adaptive Vector Quantization with Creation and Reduction Grounded in the Equinumber Principle

2005 ◽  
Vol 9 (6) ◽  
pp. 599-606 ◽  
Author(s):  
Michiharu Maeda ◽  
◽  
Noritaka Shigei ◽  
Hiromi Miyajima ◽  
Keyword(s):  
Neural Networks ◽  
Vector Quantization ◽  
Learning Process ◽  
Image Coding ◽  
Image Data ◽  
Experimental Results ◽  
Structural Learning ◽  
Adaptive Vector Quantization ◽  
Partition Space ◽  
Predetermined Number

This paper concerns the constitution of unit structures in neural networks for adaptive vector quantization. Partition errors are mutually equivalent when the number of inputs in a partition space is mutually equal, and average distortion is asymptotically minimized. This is termed the equinumber principle, in which two types of adaptive vector quantization are presented to avoid the initial dependence of reference vectors. Conventional techniques, such as structural learning with forgetting, have the same number of output units from start to finish. Our approach explicitly changes the number of output units to reach a predetermined number without neighboring relations equalling the numbers of inputs in a partition space. First, output units are sequentially created based on the equinumber principle in the learning process. Second, output units are sequentially deleted to reach a prespecified number. Experimental results demonstrate the effectiveness of these techniques in average distortion. These approaches are applied to image data and their feasibility was confirmed in image coding.

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