Academic Journals Database
Disseminating quality controlled scientific knowledge

EFFICIENT IMAGE COMPRESSION TECHNIQUE USING SELF ORGANIZING FEATURE MAPS

ADD TO MY LIST
 
Author(s): G. MOHIUDDIN BHAT | ASIFA BABA, | EKRAM KHAN

Journal: International Journal of Engineering Science and Technology
ISSN 0975-5462

Volume: 2;
Issue: 12;
Start page: 7609;
Date: 2010;
VIEW PDF   PDF DOWNLOAD PDF   Download PDF Original page

Keywords: Image Compression | JPEG | Artificial Neural Networks | SOFM

ABSTRACT
Due to the widespread use of Multimedia applications, the need for image compression is increasing day-by-day. The image compression schemes are aimed to reduce the transmission rates for still images without sacrificing much of the image quality. In this paper, an Artificial Neural Network (ANN) approach for image compression ispresented. The Codebook for Linear Vector Quantization (LVQ) is designed using Self Organized Feature Maps (SOFM). Arithmetic Coding is then used to remove redundancies between indexes of vectors corresponding to the neighboring blocks in the original image, which then leads to further compression. The simulation results demonstrate the improved coding efficiency of the proposed method, when compared with JPEG. The proposed scheme allows achieving a compression ratio upto approximately 40:1 with reasonable image quality. Further, thesimulation results demonstrate that an additional bit-rate reduction of upto approximately 30-50% can be achieved using Arithmetic Coding, without any further degradation of the image quality. When compared with JPEG, the proposed coder results reconstructed images having 0.1-0.25 dB better quality in terms of PSNR than that of JPEGcoder.
Save time & money - Smart Internet Solutions      Why do you need a reservation system?