  Recent neural compression methods have been based on the popular hyperprior
framework. It relies on Scalar Quantization and offers a very strong
compression performance. This contrasts from recent advances in image
generation and representation learning, where Vector Quantization is more
commonly employed. In this work, we attempt to bring these lines of research
closer by revisiting vector quantization for image compression. We build upon
the VQ-VAE framework and introduce several modifications. First, we replace the
vanilla vector quantizer by a product quantizer. This intermediate solution
between vector and scalar quantization allows for a much wider set of
rate-distortion points: It implicitly defines high-quality quantizers that
would otherwise require intractably large codebooks. Second, inspired by the
success of Masked Image Modeling (MIM) in the context of self-supervised
learning and generative image models, we propose a novel conditional entropy
model which improves entropy coding by modelling the co-dependencies of the
quantized latent codes. The resulting PQ-MIM model is surprisingly effective:
its compression performance on par with recent hyperprior methods. It also
outperforms HiFiC in terms of FID and KID metrics when optimized with
perceptual losses (e.g. adversarial). Finally, since PQ-MIM is compatible with
image generation frameworks, we show qualitatively that it can operate under a
hybrid mode between compression and generation, with no further training or
finetuning. As a result, we explore the extreme compression regime where an
image is compressed into 200 bytes, i.e., less than a tweet.
