Parallelization Strategy for Hierarchical Run Length Encoded Data Structures

2011 ◽  
Author(s):  
Lado Filipovic ◽  
Otmar Ertl ◽  
Siegfried Selberherr
Keyword(s):  
Data Structures ◽  
Run Length ◽  
Parallelization Strategy

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).

scholarly journals CRUSH: A New Lossless Compression Algorithm

2018 ◽  
Vol 12 (11) ◽  
pp. 406
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).

Manipulating images by using Run-length Morton Codes

1997 ◽  
Vol 11 (06) ◽  
pp. 889-907 ◽  
Author(s):  
Chi-Yen Huang ◽  
Kuo-Liang Chung
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Efficient Algorithms ◽  
Encoding Scheme ◽  
Run Length ◽  
Binary Images ◽  
Spatial Data Structures ◽  
Set Operations ◽  
Size Number

In this paper, we first present a variation of the 2-dimensional run-encoding, called the run-length Morton code encoding scheme, for compressing binary images, then we present efficient algorithms for manipulating set operations and performing conversions between the proposed encoding scheme and some well-known spatial data structures. The time complexities of set operations are linearly proportional to the size (number) of the run-length Morton codes and the time complexities of conversions are linearly proportional to the number of the nodes in the corresponding quadtree/bintree with respect to the run-length Morton codes.

Specification scheme for the visualisation of data structures

1994 ◽  
Vol 9 (3) ◽  
pp. 127
Author(s):  
X.-B. Lu ◽  
F. Stetter
Keyword(s):  
Data Structures

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

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