Learned image compression (LIC) methods have exhibited promising progress and
superior rate-distortion performance compared with classical image compression
standards. Most existing LIC methods are Convolutional Neural Networks-based
(CNN-based) or Transformer-based, which have different advantages. Exploiting
both advantages is a point worth exploring, which has two challenges: 1) how to
effectively fuse the two methods? 2) how to achieve higher performance with a
suitable complexity? In this paper, we propose an efficient parallel
Transformer-CNN Mixture (TCM) block with a controllable complexity to
incorporate the local modeling ability of CNN and the non-local modeling
ability of transformers to improve the overall architecture of image
compression models. Besides, inspired by the recent progress of entropy
estimation models and attention modules, we propose a channel-wise entropy
model with parameter-efficient swin-transformer-based attention (SWAtten)
modules by using channel squeezing. Experimental results demonstrate our
proposed method achieves state-of-the-art rate-distortion performances on three
different resolution datasets (i.e., Kodak, Tecnick, CLIC Professional
Validation) compared to existing LIC methods. The code is at
this https URL

本文提出了一种高效的并行 Transformer-CNN 混合块，用于将 CNN 的本地建模能力和 Transformer 的非本地建模能力相结合，以提高图像压缩模型的整体架构，并在熵估计模型和注意力模块方面取得了最新进展，实验结果表明，该方法在三个不同分辨率的数据集上均实现了具有创新性的率失真表现.