Conditional Probability Models for Deep Image Compression

CVPR 2018 Fabian MentzerEirikur AgustssonMichael TschannenRadu TimofteLuc Van Gool

Deep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state-of-the-art in image compression. The key challenge in learning such networks is twofold: To deal with quantization, and to control the trade-off between reconstruction error (distortion) and entropy (rate) of the latent image representation... (read more)

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