Competent Ultra Data Compression By Enhanced Features Excerption Using Deep Learning Techniques

The objective of data compression is to extract the main features of the data and to restore the decompressed data from latent space i.e., compressed data without any quality or noise. In this paper, a Convolutional LSTM model is proposed to reduce the redundancy data and unnecessary information in the data set. The proposed methodology normalizes the picture to reduce blur and noises, with a compression ratio of 50%. The Convolutional LSTM model is compared with other models such as autoencoder, denoising autoencoder, convolutional neural network and our present work shows better RMSE compared to the other models. Datasets like MNIST and other datasets are used for testing and training the images.