![]() ![]() ![]() Firstly, a method for compression by using ANN technology has to be developed to improve the design. Generally, two different categories have been put forward for enhancing the performance of compression methods. Recently, ANNs are applied in areas in which high rates of computation are essential and considered as probable solutions to problems of image compression. A NN consists of eight components: neurons, signal function, activation state vector, activity aggregation rule, pattern of connectivity, learning rule, activation rule, and environment. ANNs are employed to summarize and prototype some of the functional aspects of the human brain system in an effort so as to acquire some of its computational strengths. The architecture of ANN being drawn from the concept of brain functioning, a neural network is a hugely reticulated network of a huge number of neurons which are processing elements. The decompressed results obtained using these two algorithms are computed in terms of PSNR and MSE along with performance plots and regression plots from which it can be observed that the LM algorithm gives more accurate results than the GD algorithm.Īrtificial neural networks (ANNs) are archetypes of the biological neuron system and thus have been drawn from the abilities of a human brain. The learning methods, the Levenberg Marquardt (LM) algorithm and the Gradient Descent (GD) have been used to perform the training of the network architecture and finally, the performance is evaluated in terms of MSE and PSNR using medical images. A comparison among various feed-forward back-propagation training algorithms was presented with different compression ratios and different block sizes. NNs offer the potential for providing a novel solution to the problem of image compression by its ability to generate an internal data representation. Image compression removing redundant information in image data is a solution for storage and data transmission problems for huge amounts of data. Marked progress has been made in the area of image compression in the last decade. AbstractThis article presents an image compression method using feed-forward back-propagation neural networks (NNs). ![]()
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