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HiFiC - High-Fidelity Generative Image Compression
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We combine Generative Adversarial Networks with learned compression to obtain a state-of-the-art generative lossy compression system.
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HiFiC High-Fidelity Generative Image Compression Fabian Mentzer * ETH Zurich George Toderici Google Research Michael Tschannen ‡ Eirikur Agustsson Google Research *Work done while interning at Google Research. ‡ Work done while at Google Research. About We combine Generative Adversarial Networks with learned compression to obtain a state-of-the-art generative lossy compression system. In the paper, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In a user study, we show that our method is preferred to previous state-of-the-art approaches even if they use more than 2× the bitrate. Best viewed on a big screen. On iPhone, to move the slider while zoomed in, tap and hold until you get haptic feedback! Demo Interactive Demo comparing our method ( HiFiC - pronounced ˈhaɪˈfaɪˈsiː ) to JPG or BP...
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