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Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck
Huan Liu · George Zhang · Jun Chen · Ashish Khisti

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in Optimal Transport and Machine Learning »

We consider a generalization of the rate-distortion-perception framework for lossy compression in which the reconstruction must attain a certain target distribution. This may arise, for example, in image restoration applications when there is a bit interface between a sender who records with degraded quality and receiver who wishes to recover a clear image. In this work, we characterize this as an optimal transport problem with constrained entropy between source and target distributions. We show that optimal solutions to this problem follow a framework that partially decouples the problems of compression and transport, and demonstrate the utility of common randomness. We show that the performance of a deep learning architecture following this framework is competitive with an end-to-end system.

Author Information

Huan Liu (McMaster University)
George Zhang (University of Toronto)
Jun Chen (McMaster University)
Ashish Khisti (University of Toronto)

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