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 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^-8. A programmable analog MOS implementation provides a flexible architecture to realize the DCT-based neural net