Abstract
Image compression is one of the advantageous techniques in several types of multimedia services. And recently deep learning has been so developed that it is being used for image compression. In image compression, consider we have images of various dimensions. One such dimension is 28 by 28. Images are formed by combining red, green and blue (RGB) in various proportions to obtain any color in the visible spectrum. The image is made up of pixels and have some noise in them. We propose a Convolutional Auto encoder neural network for image compression by taking MNIST (Modern National Institute of Standards and Technology) dataset where we up sample and downs sample an image. We take an image 28 by 28 images with noise, which is an RGB image. By developing deep learning image should be compressed to 28 by 1 dimensional dense vector. After the compression final resulting image should have the original dimension of 28 by 28. The main objective is to compress an image without affecting the quality of image radically.
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Raut, Y., Tiwari, T., Pande, P., Thakar, P. (2020). Image Compression Using Convolutional Autoencoder. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_23
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DOI: https://doi.org/10.1007/978-981-15-1420-3_23
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