alexa VHDL Simulation of Image Compression Using LBG
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article

VHDL Simulation of Image Compression Using LBG

Rahul R. Ade1 Prof. Ashish B. Kharate2
  1. Department of Electronics and Telecommunication Engineering, H.V.P.M’S College of Engineering Amravati, India
  2. Department of Electronics and Telecommunication Engineering, H.V.P.M’S College of Engineering Amravati, India
Related article at Pubmed, Scholar Google
 

Abstract

Image-related communications are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Image compression is important for effective storage and transmission of images. Image compression is concerned with minimizing the number of bits required to represent an image. The algorithm used for this paper is Linde, Buzo and Gray (LBG) algorithm. It is based on minimization of the squared-error distortion measure. LBG proposed the VQ schemes for gray scale image compression. The basic requirement for this paper is codebook generation. A good codebook is required because the reconstruct image highly depends on the codeword’s in this very codebook. The generated codebook store into text file for VHDL file handling or data array in VHDL code. The VHDL file handling concept used for the quantization. This process of file handling will convert in blockwise conversion in a set of pixel value. The block array splitting by using Pairwise Nearest Neighbor (PNN) principle. The performance of compression ratio will be measure in Mean square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Analysis the synthesis result & timing summary for purpose of design parameter.

Keywords

Share This Page

Additional Info

Loading
Loading Please wait..
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords