---
# System prepended metadata

title: 'AICT: an Adaptive Image Compression Transformer.'
tags: [VAADER, Seminar]

---

# AICT: an Adaptive Image Compression Transformer.
_by Ahmed Ghorbel - 2023.11.27_

###### tags: `VAADER` `Seminar`


![vaader_seminar_ghorbel](https://hackmd.io/_uploads/B1hOZ1c4T.png)

## Video
{%youtube xycy5D30B88 %}

## Abstract
 Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute ICT. Current methods that still rely on ConvNet-based entropy coding are limited in long-range modeling dependencies due to their local connectivity and an increasing number of architectural biases and priors. On the contrary, the proposed ICT can capture both global and local contexts from the latent representations and better parameterize the distribution of the quantized latents. Further, we leverage a learnable scaling module with a sandwich ConvNeXt-based pre/post-processor to accurately extract more compact latent representation while reconstructing higher-quality images. Extensive experimental results on benchmark datasets showed that the proposed AICT framework significantly improves the trade-off between coding efficiency and decoder complexity over the VVC reference encoder (VTM-18.0) and the neural codec SwinT-ChARM.