A high-speed neural analog circuit for computing the bit-level transform image coding

A Hopfield-type neural network approach is presented which leads to an analog circuit for implementing the bit-level transform image. The computation of a 2D DCT (discrete cosine transform)-based transform coding is shown to solve a quadratic nonlinear programming problem subject to the correspondin...

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Bibliographic Details
Published inIEEE transactions on consumer electronics Vol. 37; no. 3; pp. 337 - 342
Main Authors Chang, P.R., Hwang, K.S., Gong, H.M.
Format Journal Article
LanguageEnglish
Published IEEE 01.08.1991
Subjects
Analog circuits
Analog computers
Discrete cosine transforms
Hopfield neural networks
Image coding
Image storage
Matrix decomposition
Neural networks
Quadratic programming
Two dimensional displays
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ISSN0098-3063
DOI10.1109/30.85534

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Summary:A Hopfield-type neural network approach is presented which leads to an analog circuit for implementing the bit-level transform image. The computation of a 2D DCT (discrete cosine transform)-based transform coding is shown to solve a quadratic nonlinear programming problem subject to the corresponding 2's complement binary variables of 2D DCT coefficients. A novel Hopfield-type neural analog circuit designed to perform the DCT-based quadratic nonlinear programming could obtain the desired coefficients of an 8*8 DCT in 2's complement code within 1 ns with RC=10/sup -8/. A programmable analog MOS implementation provides a flexible architecture to realize the DCT-based neural net.< >
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ISSN:0098-3063
DOI:10.1109/30.85534