Open AccessRTL Implementation of image compression techniques in WSN
Author(s) -
S. Aruna Deepthi,
E. Sreenivasa Rao,
M. H. M. Krishna Prasad
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i3.pp1750-1756
Subject(s) - computer science , non negative matrix factorization , image compression , matrix decomposition , compression ratio , peak signal to noise ratio , data compression , redundancy (engineering) , data redundancy , energy consumption , algorithm , artificial intelligence , image processing , image (mathematics) , ecology , eigenvalues and eigenvectors , physics , quantum mechanics , internal combustion engine , automotive engineering , engineering , biology , operating system
The Wireless sensor networks have limitations regarding data redundancy, power and require high bandwidth when used for multimedia data. Image compression methods overcome these problems. Non-negative Matrix Factorization (NMF) method is useful in approximating high dimensional data where the data has non-negative components. Another method of the NMF called (PNMF) Projective Nonnegative Matrix Factorization is used for learning spatially localized visual patterns. Simulation results show the comparison between SVD, NMF, PNMF compression schemes. Compressed images are transmitted from base station to cluster head node and received from ordinary nodes. The station takes on the image restoration. Image quality, compression ratio, signal to noise ratio and energy consumption are the essential metrics measured for compression performance. In this paper, the compression methods are designed using Matlab.The parameters like PSNR, the total node energy consumption are calculated. RTL schematic of NMF SVD, PNMF methods is generated by using Verilog HDL.