NDLI: Block based compressive sensing algorithm using Eigen vectors for image compression

Content Provider	IEEE Xplore Digital Library
Author	Hundet, A. Jain, R.C. Sharma, V.
Copyright Year	2014
Description	Author affiliation: Dept. of Software Syst., SATI, Vidisha, India (Jain, R.C.) \|\| Dept. of Inf. Technol., SATI, Vidisha, India (Hundet, A.; Sharma, V.)
Abstract	The image compression is widely used throughout the multimedia applications and presently many standard techniques are already available, however the in many situation (like after encryption, highly textured etc.) the data compression with the stated techniques are not sufficient, for such cases that relatively new approach called Compressive Sensing can provide a better results as recent research shows. The Compressive Sensing is a concepts primarily used for reduction in reduction in number of observation required for reconstructing the data from linear acquisition system. It fundamentally states that a linear system with N number of equations can be approximated by M equation (M <; N), if system follows sparsely condition. The paper utilizes the same concept for image compression, however the reduction in approximated system equation is performed by calculating the Eigen vectors. The application of Eigen value and vector not only simplifies the process but also provides efficient reconstruction with high compression. The simulation results also verifies the superiority proposed algorithm over previous algorithms by considerable margin.
Starting Page	1
Ending Page	5
File Size	1500381
Page Count	5
File Format	PDF
ISSN	23479337
e-ISBN	9781479963935
DOI	10.1109/ICAETR.2014.7012884
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-01
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution Dimension Reduction Image Compression Compressive Sensing Sensors Linear Systems Sparse matrices Cryptography Equations Hyperspectral imaging Biomedical imaging
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Projection matrix optimization for block-sparse compressive sensing

Measurement matrix design for hyperspectral image compressive sensing

Iteratively reweighted least squares for block-sparse recovery

Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization for Block-sparse Compressive Sensing

Compressive pushbroom and whiskbroom sensing for hyperspectral remote-sensing imaging

Kronecker product matrices for compressive sensing

TOF Depth Map Super-resolution Using Compressive Sensing

3D total variation hyperspectral compressive sensing using unmixing

Adaptive hyperspectral image compression using the KLT and integer KLT algorithms

Block based compressive sensing algorithm using Eigen vectors for image compression

Similar Documents

Projection matrix optimization for block-sparse compressive sensing

Measurement matrix design for hyperspectral image compressive sensing

Iteratively reweighted least squares for block-sparse recovery

Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization for Block-sparse Compressive Sensing

Compressive pushbroom and whiskbroom sensing for hyperspectral remote-sensing imaging

Kronecker product matrices for compressive sensing

TOF Depth Map Super-resolution Using Compressive Sensing

3D total variation hyperspectral compressive sensing using unmixing

Adaptive hyperspectral image compression using the KLT and integer KLT algorithms

Block based compressive sensing algorithm using Eigen vectors for image compression