New Artificial Neural Network Models for Bio Medical Image Compression: Bio Medical Image Compression

This article presents an image compression method using feed-forward back-propagation neural networks(NNs).Markedprogresshasbeenmadeintheareaofimagecompressioninthelastdecade. Image compression removing redundant information in image data is a solution for storage and datatransmissionproblemsforhugeamountsofdata.NNsofferthepotentialforprovidinganovel solutiontotheproblemofimagecompressionbyitsabilitytogenerateaninternaldatarepresentation. A comparison among various feed-forward back-propagation training algorithms was presented withdifferentcompressionratiosanddifferentblocksizes.Thelearningmethods,theLevenberg Marquardt(LM)algorithmandtheGradientDescent(GD)havebeenusedtoperformthetraining ofthenetworkarchitectureandfinally,theperformanceisevaluatedintermsofMSEandPSNR usingmedicalimages.Thedecompressedresultsobtainedusingthesetwoalgorithmsarecomputed intermsofPSNRandMSEalongwithperformanceplotsandregressionplotsfromwhichitcanbe observedthattheLMalgorithmgivesmoreaccurateresultsthantheGDalgorithm. KeywoRdS Artificial Neural Network, Backpropagation Neural Network, Gradient Descent Algorithm (GD), Image Compression, Levenberg Marquardt Algorithm (LM)

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