Paper:
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
Recent models for learned image compression are based on autoencoders that learn approximately invertible mappings from pixels to a quantized latent representation. The transforms are combined with an entropy model, which is a prior on the latent representation that can be used with standard arithmetic coding algorithms to generate a compressed bitstream. Recently, hierarchical entropy models were introduced as a way to exploit more structure in the latents than previous fully factorized priors, improving compression performance while maintaining end-to-end optimization. Inspired by the success of autoregressive priors in probabilistic generative models, we examine autoregressive, hierarchical, and combined priors as alternatives, weighing their costs and benefits in the context of image compression. While it is well known that autoregressive models can incur a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and can be combined to exploit the probabilistic structure in the latents better than all previous learned models. The combined model yields state-of-the-art rate-distortion performance and generates smaller files than existing methods: 15.8% rate reductions over the baseline hierarchical model and 59.8%, 35%, and 8.4% savings over JPEG, JPEG2000, and BPG, respectively. To the best of our knowledge, our model is the first learning-based method to outperform the top standard image codec (BPG) on both the PSNR and MS-SSIM distortion metrics.

Review:
Summary ======= This paper extends the autoencoder trained for compression of Balle et al. (2018) with a small autoregressive model. The autoencoder of Balle uses Gaussian scale mixtures (GSMs) for entropy encoding of coefficients, and encodes its latent variables as side information in the bit stream. Here, conditional Gaussian mixtures are used which additionally use neighboring coefficients as context. The authors find that this significantly improves compression performance.  Good ==== – Good performance (notably, state-of-the-art MS-SSIM results without optimizing directly on this metric) – Extensive supplementary materials, including rate-distortion curves for individual images – Well written  Bad === – Incremental, with no real conceptual contributions – Missing related work: There is a long history of conditional Gaussian mixture models for autoregressive modeling of images – including for entropy rate estimation – that is arguably more relevant than other generative models mentioned in the paper: Domke et al. (2008), Hosseini et al. (2010), Theis et al. (2012), Uria et al. (2013), Theis et al. (2015)