Can I read LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression on EtoBox?

LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression by Jiang, Wei; Ning, Peirong; Yang, Jiayu; Zhai, Yongqi; Gao, Feng; Wang, Ronggang is a scholarly article available to read on EtoBox.

What is LLIC: Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression about?

The effective receptive field (ERF) plays an important role in transform coding, which determines how much redundancy can be removed during transform and how many spatial priors can be utilized to synthesize textures during inverse transform. Existing methods rely on stacks of small kernels, whose ERFs remain insufficiently large, or heavy non-local attention mechanisms, which limit the potential of high-resolution image coding. To tackle this issue, we propose Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression (LLIC). Specifically, for the first time in the learned image compression community, we introduce a few large kernelbased depth-wise convolutions to reduce more redundancy while maintaining modest complexity. Due to the wide range of image diversity, we further propose a mechanism to augment convolution adaptability through the self-conditioned generation of weights. The large kernels cooperate with non-linear embedding and gate mechanisms for better expressiveness and lighter pointwise interactions. Our investigation extends to refined training methods that unlock the full potential of these large kernels. Moreover, to promote more dy

Author

Jiang, Wei; Ning, Peirong; Yang, Jiayu; Zhai, Yongqi; Gao, Feng; Wang, Ronggang

Published

2023

Language

EN