Grouped Transform for Ultra-Low-Complexity Learned Image Compression

Existing learned image compression (LIC) methods have shown strong performance advantages but also bring high computational complexity, making it challenging to deploy them on resource-constrained devices. To reduce the high computational and storage cost, we propose a fully grouped image compression network by introducing spatial and channel grouping operations. Grouping operation is helpful to obtain compact representations by aggregating similar features and reducing redundancy between features in LIC task. Specifically, our proposed network consists of two efficient parts, one is the spatial grouping transform for spatial resolution sampling, and the other is the channel grouping transform for nonlinear representation capability enhancement. Moreover, convolutional kernel factorization and inverted bottleneck are used to reduce redundancy and enrich information of each group in the channel grouping transform, which achieve a good balance between computational complexity and network performance. Experimental results show that our method not only achieves competitive rate-distortion performance with fewer KMACs/pixel and model parameters, but also reduces the real-world runtime. In particular, our proposed models provide at least over 84.6% computational complexity reduction when compared with several advanced LIC methods.