end-to-end optimized image compression has emerged as a disruptive technique to reduce the spatial redundancies with an improved reconstruction quality. However, existing entropy model for latent representations canno...
详细信息
ISBN:
(纸本)9781728180687
end-to-end optimized image compression has emerged as a disruptive technique to reduce the spatial redundancies with an improved reconstruction quality. However, existing entropy model for latent representations cannot sufficiently exploit their spatial and channel-wise correlations. In this paper, we propose a novel entropy model based on spatial-channel contexts for end-to-end optimized image compression. The proposed model jointly leverages spatial structural dependencies and channel-wise correlations to improve the probabilistic estimation of latent representations. Instead of complex autoregressive hyperprior network, shallow artificial neural networks (ANNs) incorporating 3-D masks are developed to efficiently realize the entropy model with a guarantee of causality. Experimental results demonstrate that the proposed model achieves competitive rate-distortion performance and reduces model complexity in comparison to recent end-to-endoptimized methods for imagecompression.
暂无评论