Parallel Neural Local Lossless Compression. (arXiv:2201.05213v1 [eess.IV])

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
a parallelization scheme for local autoregressive models. We discuss the
practicalities of implementing this scheme, and provide experimental evidence
of significant gains in compression runtime compared to …

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