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Recent state-of-the-art Learned Image Compression methods feature spatialcontext models, achieving great rate-distortion improvements over hyperpriormethods. However, the autoregressive context model requires serial decoding,limiting runtime performance. The Checkerboard context model allows paralleldecoding at a cost of reduced RD performance. We present a series of multistagespatial context models allowing both fast decoding and better RD performance.We split the latent space into square patches and decode serially within eachpatch while different patches are decoded in parallel. The proposed methodfeatures a comparable decoding speed to Checkerboard while reaching the RDperformance of Autoregressive and even also outperforming Autoregressive.Inside each patch, the decoding order must be carefully decided as a bad ordernegatively impacts performance; therefore, we also propose a decoding orderoptimization algorithm.

Multistage Spatial Context Models for Learned Image Compression