  Learned image compression methods have shown superior rate-distortion
performance and remarkable potential compared to traditional compression
methods. Most existing learned approaches use stacked convolution or
window-based self-attention for transform coding, which aggregate spatial
information in a fixed range. In this paper, we focus on extending spatial
aggregation capability and propose a dynamic kernel-based transform coding. The
proposed adaptive aggregation generates kernel offsets to capture valid
information in the content-conditioned range to help transform. With the
adaptive aggregation strategy and the sharing weights mechanism, our method can
achieve promising transform capability with acceptable model complexity.
Besides, according to the recent progress of entropy model, we define a
generalized coarse-to-fine entropy model, considering the coarse global
context, the channel-wise, and the spatial context. Based on it, we introduce
dynamic kernel in hyper-prior to generate more expressive global context.
Furthermore, we propose an asymmetric spatial-channel entropy model according
to the investigation of the spatial characteristics of the grouped latents. The
asymmetric entropy model aims to reduce statistical redundancy while
maintaining coding efficiency. Experimental results demonstrate that our method
achieves superior rate-distortion performance on three benchmarks compared to
the state-of-the-art learning-based methods.
