Reversible Colour Density Compression of Images using cGANs
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Abstract (arXiv)
Image compression using colour densities is historically impractical to decompress losslessly. We examine the use of conditional generative adversarial networks in making this transformation more feasible, through learning a mapping between the images and a loss function to train on. We show that this method is effective at producing visually lossless generations, indicating that efficient colour compression is viable.
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References · 7
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2016Improved Techniques for Training GANscited by 2,970
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2018Generative Adversarial Networks for Extreme Learned Image Compressioncited by 219
- 2014Jpeg Image Compression Using Discrete Cosine Transform - A Surveycited by 11
- 2018Near-lossless ℓ_∞-constrained Image Decompression via Deep Neural Networkcited by 5
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