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)

PDF Abstract CVPR 2018 PDF CVPR 2018 Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet