Lossless Compression Method Using Quadtree and 4-direction Differential Pulse Coding Modulation for Hologram Data with Fourier Transform Optical System

2020 ◽  
Author(s):  
Hiroki Oi ◽  
Yuji Sakamoto
Keyword(s):  
Fourier Transform ◽  
Optical System ◽  
Lossless Compression ◽  
Differential Pulse ◽  
Compression Method

A new lossless compression method using Direction Adaptive-Discrete wavelet transform and Modified SPIHT coding

2021 ◽  
Author(s):  
N. Karthika Devi ◽  
G. Mahendran ◽  
S. Murugeswari ◽  
S. Praveen Samuel Washburn ◽  
D. Archana Devi ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Lossless Compression ◽  
Discrete Wavelet ◽  
Compression Method ◽  
Spiht Coding

scholarly journals GPU-Based Lossless Compression of Aurora Spectral Data using Online DPCM

2019 ◽  
Vol 11 (14) ◽  
pp. 1635 ◽  
Author(s):  
Jiaojiao Li ◽  
Jiaji Wu ◽  
Gwanggil Jeon
Keyword(s):  
Spectral Data ◽  
Matrix Multiplication ◽  
Lossless Compression ◽  
Differential Pulse ◽  
Acquisition Time ◽  
Time Interval ◽  
Processing Unit ◽  
Pulse Code Modulation ◽  
Differential Pulse Code Modulation ◽  
Redundant Data

It is well known that aurorae have very high research value, but the data volume of aurora spectral data is very large, which brings great challenges to storage and transmission. To alleviate this problem, compression of aurora spectral data is indispensable. This paper presents a parallel Compute Unified Device Architecture (CUDA) implementation of the prediction-based online Differential Pulse Code Modulation (DPCM) method for the lossless compression of the aurora spectral data. Two improvements are proposed to improve the compression performance of the online DPCM method. One is on the computing of the prediction coefficients, and the other is on the encoding of the residual. In the CUDA implementation, we proposed a decomposition method for the matrix multiplication to avoid redundant data accesses and calculations. In addition, the CUDA implementation is optimized with a multi-stream technique and multi-graphics processing unit (GPU) technique, respectively. Finally, the average compression time of an aurora spectral image reaches about 0.06 s, which is much less than the 15 s aurora spectral data acquisition time interval and can save a lot of time for transmission and other subsequent tasks.

Balloon-borne GLORIA hyperspectral Limb and Nadir imager in the LWIR

2021 ◽  
Author(s):  
Erik Kretschmer ◽  
Felix Friedl-Vallon ◽  
Thomas Gulde ◽  
Michael Höpfner ◽  
Sören Johansson ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Optical System ◽  
Late Summer ◽  
High Spectral Resolution ◽  
Fourier Transform Spectrometer ◽  
System Improvement ◽  
The Fourier Transform ◽  
Temperature Retrieval ◽  
Long Wave Infrared ◽  
Better Than

<p>The GLORIA-B (Gimballed Limb Observer for Radiance Imaging of the Atmosphere - Balloon) instrument is an adaptation of the very successful GLORIA-AB imaging Fourier transform spectrometer (iFTS) flown on the research aircrafts HALO and M55 Geophysica. The high spectral resolution in the LWIR (Long Wave Infrared) allows for the retrieval of temperature and of a broad range of atmospheric trace gases, with the goal to retrieve O<sub>3</sub>, H<sub>2</sub>O, HNO<sub>3</sub>, C<sub>2</sub>H<sub>6</sub>, C<sub>2</sub>H<sub>2</sub>, HCOOH, CCl<sub>4</sub>, PAN, ClONO<sub>2</sub>, CFC-11, CFC-12, SF<sub>6</sub>, OCS, NH<sub>3</sub>, HCN, BrONO<sub>2</sub>, HO<sub>2</sub>NO<sub>2</sub>, N<sub>2</sub>O<sub>5</sub> and NO<sub>2</sub>. The radiometric sensitivity of the Balloon instrument is further increased in comparison with the GLORIA-AB instrument by having two detector channels on the same focal plane array, while keeping the same concept of a cooled optical system. This system improvement was achieved with minimal adaptation of the existing optical system.</p><p>The high spatial and temporal resolution of the instrument is ensured by the imaging capability of the Fourier transform spectrometer while stabilizing the line-of-sight in elevation with the instrument and in azimuth with the balloon gondola. In a single measurement lasting 13 seconds, the atmosphere can be sounded from mid-troposphere up to flight altitude, typically 30 km, with a vertical resolution always better than 1 km for most retrieved species; a spatial resolution up to 0.3 km can be achieved in favourable conditions. Temperature retrieval precision between 0.1 and 0.2 K is expected. A spectral sampling up to 0.0625 cm<sup>-1</sup> can be achieved.</p><p>The first flight of GLORIA-B shall take place during the late-summer polar jet turn-around at Kiruna/ESRANGE. This flight is organised in the frame of the HEMERA project and was scheduled for summer 2020, but was ultimately postponed to summer 2021. Beyond qualification of the first balloon-borne iFTS, the scientific goals of the flight are, among others, the quantification of the stratospheric bromine budget and its diurnal evolution by measuring vertical profiles of BrONO<sub>2 </sub>in combination with BrO observations by the DOAS instrument of University Heidelberg on the same platform.</p>

A Near-lossless Compression Method Based on CCSDS for Satellite Images

2012 ◽  
Vol 4 (18) ◽  
pp. 226-235
Author(s):  
ShenChuan Tai ◽  
TseMing Kuo ◽  
ChengHan Ho ◽  
TzuWen Liao
Keyword(s):  
Satellite Images ◽  
Lossless Compression ◽  
Compression Method

scholarly journals Impact of Misclassification Rates on Compression Efficiency of Red Blood Cell Images of Malaria Infection Using Deep Learning

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1062 ◽  
Author(s):  
Yuhang Dong ◽  
W. David Pan ◽  
Dongsheng Wu
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Red Blood Cell ◽  
Malaria Infection ◽  
Public Health Problem ◽  
Lossless Compression ◽  
Compression Method ◽  
Misclassification Rates ◽  
The Impact ◽  
Image Datasets

Malaria is a severe public health problem worldwide, with some developing countries being most affected. Reliable remote diagnosis of malaria infection will benefit from efficient compression of high-resolution microscopic images. This paper addresses a lossless compression of malaria-infected red blood cell images using deep learning. Specifically, we investigate a practical approach where images are first classified before being compressed using stacked autoencoders. We provide probabilistic analysis on the impact of misclassification rates on compression performance in terms of the information-theoretic measure of entropy. We then use malaria infection image datasets to evaluate the relations between misclassification rates and actually obtainable compressed bit rates using Golomb–Rice codes. Simulation results show that the joint pattern classification/compression method provides more efficient compression than several mainstream lossless compression techniques, such as JPEG2000, JPEG-LS, CALIC, and WebP, by exploiting common features extracted by deep learning on large datasets. This study provides new insight into the interplay between classification accuracy and compression bitrates. The proposed compression method can find useful telemedicine applications where efficient storage and rapid transfer of large image datasets is desirable.

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