LVPNet reports lower bits-per-pixel than prior learned lossless codecs by conditioning pixel predictions on a global multi-scale latent variable with a quantization compensation module.
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models
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abstract
We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images. We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results.
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LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
LVPNet reports lower bits-per-pixel than prior learned lossless codecs by conditioning pixel predictions on a global multi-scale latent variable with a quantization compensation module.