Efficient Lossless Compression with Distribution Quantized Finite-State Autoregressive Model

Learned lossless image compression has been a popular topic in recent research. While outperforming many traditional compression techniques in terms of compression ratio, the high time and space complexity of such methods prevent them from wider applications. Recently, Finite-State AutoRegressive (FSAR) entropy coding was proposed, boosting autoregressive entropy coding for the quantized latent space utilizing a lookup table. However, such a method still consumes an extraordinary amount of memory, which prevents its application on low-end devices. In this work, we propose the Distribution Quantized FSAR (DQ-FSAR) model, building a two-step lookup table for the FSAR model that significantly reduces memory occupation. Furthermore, we design compact loss functions to optimize the DQ-FSAR model. Experiments show that our method reduces more than 99% memory usage compared to the baseline, with a similar compression ratio and speed.