AbstractIn this research, adaptive Two-Dimensional Multilayer Neural Network (TDMNN) architecture is proposed, designed and implemented for image compression and decompression. The adaptive TDMNN architecture performs image compression and decompression by automatically choosing one of the three (linear, nonlinear and hybrid) TDMNN architectures based on input image entropy and required compression ratio. The architecture is two-dimensional, 2D to 1D reordering of input image is avoided, as the TDMNN architecture is implemented using hybrid neural network, analog to digital conversion of image input is eliminated. The architecture is trained to reconstruct images in the presence of noise as well as channel errors.
Software reference model for Adaptive TDMNN architecture is designed and modeled using Matlab. Modified backpropagation algorithm that can train two-dimensional network is proposed and is used to train the TDMNN architecture. Performance metrics such as Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) are computed and compared with well established DWT-SPIHT technique. There is 10% to 25% improvement in reconstructed image quality measured in terms of MSE and PSNR compared to DWT-SPIHT technique. Software reference model results show that the compression and decompression time for TDMNN architecture is less than 25 ms for image of size 256 x 256, which is 60 times faster than DWT-SPIHT technique.
Based on weights and biases of the network obtained from the software reference model VLSI implementation of adaptive TDMNN architecture is carried out. A new hybrid multiplying DAC is designed that multiplies current intensities (analog input) with digital weights. The hybrid multiplier is integrated with adder and network function to realize a hybrid neuron cell. The hybrid neuron cell designed using 1420 transistors works at 200 MHz, consuming less than 232 mW of power, with full scale current of 65.535 μA. Multiple hybrid neurons are integrated together to realize the 2-D adaptive multilayer neural network architecture.
|Date of Award||2010|
|Supervisor||Yuri Vershinin (Supervisor)|