Exploring High Dimensional Feature Space With Channel‐Spatial Nonlinear Transforms for Learned Image Compression

ABSTRACT Nonlinear transforms have significantly advanced learned image compression (LIC), particularly using residual blocks. This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field, which indicates how the convolution proc...

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Bibliographic Details

Published in:	CAAI Transactions on Intelligence Technology
Main Authors:	Wen Tan, Fanyang Meng, Chao Li, Youneng Bao, Yongsheng Liang
Format:	Article
Language:	English
Published:	Wiley 2025-08-01
Subjects:	high dimensional feature space learned image compression nonlinear transform the dimension increase and decrease
Online Access:	https://doi.org/10.1049/cit2.70025