THRESHOLD MODELS FOR SKIPPING ENTROPY CODING IN END-TO-END IMAGE COMPRESSION USING NEURAL NETWORKS

Abstract

Methods are described for determining thresholds to skip entropy coding of latent features in image and video coding using a neural network. A threshold based on a mean of estimated standard deviations of all latents is proposed. For autoregressive neural networks, to avoid drift between context model parameter estimates computed during training and inference, it is proposed to have two separate entropy parameter estimation networks: an initial entropy-estimation network, to be used in entropy skip encoding and decoding of quantized latents, and a refined entropy-estimation network, to be used in arithmetic encoding and decoding of the quantized latents. Methods and systems for multi-stage entropy skipping are also proposed.

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IPC Classification

H04N 19/ 132 A I H04N 19/ 91 A I