.png){kind=icon}
.png){kind=icon}
Return
Parallel lossless HSI compression based on RLS filter
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jpdc.2020.12.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The recent advancement in the field of electronics has led to development of sensors that capture the image of an area or object in spectral-domain along with spatial information. Due to continuity of spectral domain in hyperspectral images, it is difficult to store, process, analyze or transmit the critical information contained in it. Prediction based compression technique is used to reduce this size by a certain level. It predicts the value of a pixel with some error from previous pixels using a filter and finally, encode that error using variable length encoder. The execution time taken by this technique is very high which can be reduced by high performance computing. In this paper, we designed a mechanism to use high-performance computing techniques in the execution of prediction based image compression algorithms. The average execution time of the RLS-filter based compression algorithm is reduced significantly (by a factor of 29 using 2 nodes with 28 cores each, on PARAM SHIVAY supercomputer) with the proposed technique. (c) 2020 Elsevier Inc. All rights reserved.
Keywords:
Multithreading
Multiprocessing
Hyperspectral image compression
RLS filter
Prediction based compression
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
3.8K
Citations:
4.8K