Deep Learning Based Image Compression with Generative Models (GANs)

Data compression is an area that reduces data volume, so we can save as much memory as you would without compression. Examples of data that we want to compress can be audio files, videos, images, and more. This project deals with image compression.
Compression can be distinguished from two major types: Lossless Compression and Lossy Compression. In Compression Shift, compressed information is completely restored to the original information. In contrast, compression loses the reconstructed information very close but not the same as the original information.
There are several common image compression formats, one of which is JPEG - the Joint Photographic Expert Group. In recent years, neuron networks have become the most popular models in artificial intelligence. They excel in decision making. A network of neurons can easily identify and classify items in an image, and hence comes the desire to use these models to overcome the problems that emerge in past compression methods.
In this project, we will focus on an existing method that attempts to achieve good compression results using generative models, and more specifically through GAN, we will focus on the limitations of this method and try to improve it to produce better results.
The emphasis of the project is to achieve a stochastic generator in the GAN compression and recovery system, a representative of a new method that achieves this goal while overcoming the limitations of the other solutions that we will present during the project.

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Deep Learning Based Image Compression with Generative Models (GANs)
Deep Learning Based Image Compression with Generative Models (GANs)