  The recently proposed Neural Local Lossless Compression (NeLLoC), which is
based on a local autoregressive model, has achieved state-of-the-art (SOTA)
out-of-distribution (OOD) generalization performance in the image compression
task. In addition to the encouragement of OOD generalization, the local model
also allows parallel inference in the decoding stage. In this paper, we propose
two parallelization schemes for local autoregressive models. We discuss the
practicalities of implementing the schemes and provide experimental evidence of
significant gains in compression runtime compared to the previous, non-parallel
implementation.
