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arXiv · 2105.07512

Substitutional Neural Image Compression

Xiao Wang·Wei Jiang·Wei Wang·Shan Liu·Brian Kulis·Peter Chin
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Abstract

We describe Substitutional Neural Image Compression (SNIC), a general approach for enhancing any neural image compression model, that requires no data or additional tuning of the trained model. It boosts compression performance toward a flexible distortion metric and enables bit-rate control using a single model instance. The key idea is to replace the image to be compressed with a substitutional one that outperforms the original one in a desired way. Finding such a substitute is inherently difficult for conventional codecs, yet surprisingly favorable for neural compression models thanks to their fully differentiable structures. With gradients of a particular loss backpropogated to the input, a desired substitute can be efficiently crafted iteratively. We demonstrate the effectiveness of SNIC, when combined with various neural compression models and target metrics, in improving compression quality and performing bit-rate control measured by rate-distortion curves. Empirical results of control precision and generation speed are also discussed.

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Xiao Wang, Wei Jiang, Wei Wang, Shan Liu, Brian Kulis, Peter Chin. 2021-05-16. Substitutional Neural Image Compression. https://arxiv.org/abs/2105.07512

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