Reviews: Joint Autoregressive and Hierarchical Priors for Learned Image Compression

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)