Title
Image Compression by Orthogonal Decomposition Using Cellular Neural Network Chips
Abstract
In the paper a new hardware architecture for the implementation of a high-speed, low bit-rate image coding system is outlined. Our proposed algorithm is based on the Cellular Neural/Nonlinear Network (CNN) chip-set. A simple and fast method is introduced to generate basis functions of 2 dimensional (2D) orthogonal transformations. Using these 2D basis functions of the Hadamard or Cosine functions, the transformation coefficients of the basic blocks of the image are measured by the CNN. Meanwhile, the CNN can produce the inverse transformation of the measured coefficients and the actual distortion-rate can be computed. If a required distortion-rate is reached, the coding process could be stopped (the use of even more coefficients would increase bit-rate needlessly). Effects of noise and VLSI computing accuracy are also considered to optimise the architecture. Hardware architecture and operational scheme of the CNN-based coding/decoding system. The CNN is the basic processor to measure the coefficients of the orthogonal transformation, while it calculates the inverse transformation as well. Error-rate and bit-rate are measured in-flight, as the coefficients of the spatial frequencies are estimated sequentially.
Year
DOI
Venue
1999
10.1002/(SICI)1097-007X(199901/02)27:1<117::AID-CTA44>3.0.CO;2-H
International Journal of Circuit Theory and Applications
Keywords
Field
DocType
coding process,cnn-based coding,new hardware architecture,hardware architecture,low bit-rate image,transformation coefficient,cellular neural network chips,image compression,basis function,orthogonal transformation,orthogonal decomposition,measured coefficient,inverse transformation,chip,error rate,cellular neural network
Orthogonal transformation,Computer science,Algorithm,Image processing,Electronic engineering,Basis function,Data compression,Cellular neural network,Hadamard transform,Image compression,Hardware architecture
Journal
Volume
Issue
ISSN
27
1
0098-9886
Citations 
PageRank 
References 
3
0.51
3
Authors
2
Name
Order
Citations
PageRank
Sziranyi, T.139544.76
László Czuni26813.41