A Transformer-based Image Compression (TIC) approach is developed which
reuses the canonical variational autoencoder (VAE) architecture with paired
main and hyper encoder-decoders. Both main and hyper encoders are comprised of
a sequence of neural transformation units (NTUs) to analyse and aggregate
important information for more compact representation of input image, while the
decoders mirror the encoder-side operations to generate pixel-domain image
reconstruction from the compressed bitstream. Each NTU is consist of a Swin
Transformer Block (STB) and a convolutional layer (Conv) to best embed both
long-range and short-range information; In the meantime, a casual attention
module (CAM) is devised for adaptive context modeling of latent features to
utilize both hyper and autoregressive priors. The TIC rivals with
state-of-the-art approaches including deep convolutional neural networks (CNNs)
based learnt image coding (LIC) methods and handcrafted rules-based intra
profile of recently-approved Versatile Video Coding (VVC) standard, and
requires much less model parameters, e.g., up to 45% reduction to
leading-performance LIC.

本文提出了一种基于 Transformer 的图像压缩方法（TIC），使用深度神经网络来分析和压缩输入图像，与最先进的基于卷积神经网络和基于人工编码规则压缩算法相比，该方法具有更少的参数个数，达到了同等性能。