A Comparative Study of Lossless Compression Algorithms on Multi-spectral Imager Data

2009 ◽  
Author(s):  
M. Grossberg ◽  
I. Gladkova ◽  
S. Gottipati ◽  
M. Rabinowitz ◽  
P. Alabi ◽  
...  
Keyword(s):  
Comparative Study ◽  
Lossless Compression ◽  
Compression Algorithms ◽  
Spectral Imager

A comparative study of lossless compression algorithms on MODIS data

2007 ◽  
Author(s):  
Srikanth Gottipati ◽  
Jamal Goddard ◽  
Michael Grossberg ◽  
Irina Gladkova
Keyword(s):  
Comparative Study ◽  
Lossless Compression ◽  
Compression Algorithms ◽  
Modis Data

A comparative study of lossless compression algorithms on multispectral imager data

2009 ◽  
Author(s):  
Michael Grossberg ◽  
Srikanth Gottipati ◽  
Irina Gladkova ◽  
Malka Rabinowitz ◽  
Paul Alabi ◽  
...  
Keyword(s):  
Comparative Study ◽  
Lossless Compression ◽  
Compression Algorithms ◽  
Multispectral Imager

scholarly journals Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression

2019 ◽  
Vol 11 (21) ◽  
pp. 2461 ◽  
Author(s):  
Kevin Chow ◽  
Dion Tzamarias ◽  
Ian Blanes ◽  
Joan Serra-Sagristà
Keyword(s):  
Data Structure ◽  
Hyperspectral Image ◽  
Lossless Compression ◽  
Hyperspectral Images ◽  
Direct Access ◽  
Bit Rate ◽  
Compression Algorithms ◽  
Real Time Processing ◽  
Time Processing ◽  
Differential Encoding

This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.

scholarly journals An Optimal Seed Based Compression Algorithm for DNA Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Pamela Vinitha Eric ◽  
Gopakumar Gopalakrishnan ◽  
Muralikrishnan Karunakaran
Keyword(s):  
Dna Sequence ◽  
Dna Sequences ◽  
Lossless Compression ◽  
Compression Algorithm ◽  
Compression Scheme ◽  
Substitution Method ◽  
Compression Algorithms ◽  
Dna Sequence Compression ◽  
Sequence Compression ◽  
Better Than

This paper proposes a seed based lossless compression algorithm to compress a DNA sequence which uses a substitution method that is similar to the LempelZiv compression scheme. The proposed method exploits the repetition structures that are inherent in DNA sequences by creating an offline dictionary which contains all such repeats along with the details of mismatches. By ensuring that only promising mismatches are allowed, the method achieves a compression ratio that is at par or better than the existing lossless DNA sequence compression algorithms.

scholarly journals Aird: A computation-oriented mass spectrometry data format enables higher compression ratio and less decoding time

2020 ◽  
Author(s):  
Miaoshan Lu ◽  
Shaowei An ◽  
Ruimin Wang ◽  
Jinyin Wang ◽  
Changbin Yu
Keyword(s):  
Mass Spectrometry ◽  
Lossless Compression ◽  
Mass Spectrometry Data ◽  
Compression Rate ◽  
File Size ◽  
Data Format ◽  
Compression Algorithms ◽  
Link Type ◽  
Processing Algorithms ◽  
Data Independence

ABSTRACTWith the precision of mass spectrometer going higher and the emergence of data independence acquisition (DIA), the file size is increasing rapidly. Beyond the widely-used open format mzML (Deutsch 2008), near-lossless or lossless compression algorithms and formats have emerged. The data precision is often related to the instrument and subsequent processing algorithms. Unlike storage-oriented formats, which focusing more on lossless compression and compression rate, computation-oriented formats focus as much on decoding speed and disk read strategy as compression rate. Here we describe “Aird", an opensource and computation-oriented format with controllable precision, flexible indexing strategies and high compression rate. Aird uses JavaScript Object Notation (JSON) for metadata storage, multiple indexing, and reordered storage strategies for higher speed of data randomly reading. Aird also provides a novel compressor called Zlib-Diff-PforDelta (ZDPD) for m/z data compression. Compared with Zlib only, m/z data size is about 65% lower in Aird, and merely takes 33% decoding time.AvailabilityAird SDK is written in Java, which allow scholars to access mass spectrometry data efficiently. It is available at https://github.com/Propro-Studio/Aird-SDK AirdPro can convert vendor files into Aird files, which is available at https://github.com/Propro-Studio/AirdPro

scholarly journals CRUSH: A New Lossless Compression Algorithm

2018 ◽  
Vol 12 (11) ◽  
pp. 387
Author(s):  
Evon Abu-Taieh ◽  
Issam AlHadid
Keyword(s):  
Data Structures ◽  
Time Complexity ◽  
Lossless Compression ◽  
Haar Wavelet ◽  
Compression Algorithm ◽  
Arithmetic Coding ◽  
Huffman Coding ◽  
Compression Algorithms ◽  
Run Length

Multimedia is highly competitive world, one of the properties that is reflected is speed of download and upload of multimedia elements: text, sound, pictures, animation. This paper presents CRUSH algorithm which is a lossless compression algorithm. CRUSH algorithm can be used to compress files. CRUSH method is fast and simple with time complexity O(n) where n is the number of elements being compressed.Furthermore, compressed file is independent from algorithm and unnecessary data structures. As the paper will show comparison with other compression algorithms like Shannon–Fano code, Huffman coding, Run Length Encoding, Arithmetic Coding, Lempel-Ziv-Welch (LZW), Run Length Encoding (RLE), Burrows-Wheeler Transform.Move-to-Front (MTF) Transform, Haar, wavelet tree, Delta Encoding, Rice &Golomb Coding, Tunstall coding, DEFLATE algorithm, Run-Length Golomb-Rice (RLGR).

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