Uncompressed multimedia data occupy more
storage space and very high data rates for transmission.
Innovation is always under process to derive new high speed and
efficient image compression methodologies. The main aim of
this paper is to determine suitable wavelet for image
compression using hybrid wavelet multi neural network
architecture. A detailed comprehensive experimental analysis is
carried out on the proposed hybrid architecture to obtain better
fidelity metrics. The process involved applying different hand
designed wavelet transforms on the proposed hybrid architecture
and computing the objective fidelity metrics by performing
necessary changes in different bands of frequency coefficients
obtained, every time the wavelet is changed. This process is
repeated with various wavelets such as the Haar Wavelet,
Daubechies Wavelets, Coiflets, Symlets and the Biorthogonal
wavelets etc. Wide range of gray scale and colour images of
varying details are considered and Performance metrics obtained
are tabulated and analyzed graphically.
Sridhar S : is currently a research scholar at JNTU Kakinada, Andhra
Pradesh, India. He received his Degree in Electronics and
Communication Engineering in 2000 and Mtech from JNTUHyderabad.
He is having around 13 years of teaching experience. His
areas of interest are Low Power VLSI Design Compression
Architectures, Digital Image Processing and neural networks etc.
Rajesh Kumar P : is currently working as Professor, Department of
Electronics And Communication Engineering and Assistant Principal
at Andhra University College of Engineering, Andhra University,
Visakhapatnam. He published papers in many Reputed International
Journals and various national, international conferences. He has
teaching Experience of around 11 years. His research areas of
interest are Radar Signal Processing, Digital Signal Processing and
Digital Image Processing etc.
Ramanaiah K V : is currently working as Associate Professor and
HOD of Electronics and Communication Engineering Department at
YSR Engineering College of Yogi Vemana University. He received his
Mtech and PhD from JNTU Hyderabad. He published papers in
Many Reputed international Journals and various national,
international conferences. He has teaching experience of around 21
years. His research areas of interest are Digital Image Processing,
VLSI Architectures and Neural Networks etc.
Image Compression
Hand Designed Wavelets
MLP
NN
PSNR
MSE
Experimental analysis is performed to select one
appropriate wavelet filter function that can produce better
fidelity metrics (PSNR and MSE, %CR)etc., with the
proposed Hybrid Predictive coded Wavelet Neural
Network Architecture for image compression. Different
hand designed wavelet transform filter functions like
HAAR, Daubechies (DbN, N= 1 to 10), Coiflets ( Coif1 to
Coif5), Symlets (Sym1 to Sym8) etc., are used for
performing the analysis. Experimental analysis performed
by applying every wavelet filter function in the
architecture and doing the necessary changes in the
architecture in order to accommodate the non symmetrical
nature of filter function used for analysis because each
filter function will produce coefficients of different
matrices.
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