Toward Variable-Rate Generative Compression by Reducing the Channel Redundancy

Abstract

Compressing large images with a generative model goes beyond typical image encoding standards under a notably low bitrate. In this paper, we step toward practical generative compression systems based on recent advances. Specifically, we show that the channel redundancy of the latent representation produced by an autoencoder network can be effectively compressed via mask compression. The mask compression performs quantization on the channel variance of latent representation instead of original values. Instead of training multiple models, changing the mask leads to a simple and efficient variable rate compression scheme. Then, we estimate the relative bitrate by measuring the L1 norm of the channel variance and hence obtain the rate-distortion formulation. The L1 regularizer assumes a Laplacian prior on the channel variance, through which model we develop corresponding methods to produce approximate images at a target bitrate. This eliminates the need for manually searching hyperparameters for our variable-rate compression. We conduct exhaustive experiments to demonstrate the advanced performance of the proposed method in preserving image quality and semantics.