Abstract
Most image compression algorithms rely on custom-built encoder–decoder pairs, and they lack flexibility and are differing to the data being compressed. In this paper, we have elaborated on the notion of generative compression by implementing various compression techniques using a generative adversarial network. We have also analysed the compression approaches that are implemented using deep learning. Our experiments are performed on the handwritten digits database and are yielding progressive results with both conditional and quantifiable benchmarks.
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Adate, A., Saxena, R., Gladys Gnana Kiruba, B. (2019). Analysing Image Compression Using Generative Adversarial Networks. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_33
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DOI: https://doi.org/10.1007/978-981-13-1595-4_33
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