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Wavelet based Effective Color Image Compression using Neural Networks and Modified RLC

P. Sreenivasulu, Dr.K. Anitha Sheela, K. Penchalaiah


Image compression is a technique of reducing the size
of image by eliminating data redundancy. It helps in reducing the amount of memory required to store an image and the time required to transmit the image over long distance. Earlier image compression
is performed by using wavelet and neural network. This paper proposes a method for image compression that uses wavelet and Multilayer Feed forward neural network (MLFFN) with Error Back
Propagation algorithm (EBPA), which is used to train multi layer feed forward neural network with an excellent input and output mapping. This algorithm is used for LL2 component and Modified Run Length Coding (RLC) to LH2, HL2 components with hard
threshold to discard insufficient coefficients. Performance of proposed image compression method is evaluated using Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Mean Square Error
(MSE). These estimation parameters were found to be greater when compared to image compression methods SOFM, EZW, SPIHT.


Image Compression, Wavelet, MLFFNN, EBP

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