Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression

arXiv ｜ Paper

Background

The latest advancements in neural image compression show great potential in surpassing the rate-distortion performance of conventional standard codecs. Nevertheless, there exists an indelible domain gap between the datasets utilized for training (i.e., natural images) and those utilized for inference (e.g., artistic images).

Figure 1. (upper left) Three state-of-the-art neural image codecs perform fairly well on the Kodak dataset. (upper right) But their performance drops significantly on an out-of-domain dataset containing artistic images.

Method

Our proposal involves a low-rank adaptation approach aimed at addressing the rate-distortion drop observed in out-of-domain datasets. Specifically, we perform low-rank matrix decomposition to update certain adaptation parameters of the client's decoder. These updated parameters, along with image latents, are encoded into a bitstream and transmitted to the decoder in practical scenarios. Due to the low-rank constraint imposed on the adaptation parameters, the resulting bit rate overhead is small. Furthermore, the bit rate allocation of low-rank adaptation is non-trivial, considering the diverse inputs require varying adaptation bitstreams. We thus introduce a dynamic gating network on top of the low-rank adaptation method, in order to decide which decoder layer should employ adaptation. The dynamic adaptation network is optimized end-to-end using rate-distortion loss.

Figure 2. Simplified pipeline of our dynamic instance adaptive method.

Requirements

pip install compressai
pip install timm==0.6.7 dataclasses==0.8

Dataset

Our self-collected 100 pixel-style gaming images are available at google drive.

Quick Start

sh eval.sh

or custom the config

CUDA_VISIBLE_DEVICES=0 python3 eval.py \
        --lambda {Bit-rate distortion parameter} \ 
        --quality {reconstruction quality, range [1, 6]} \
        -m cheng2020-attn \
        --epochs {optimize iteration, default 2000} \
        -lr {learning rate of low-rank decompose parameters, default 1e-3} \
        --cuda \
        --model_prefix {path to save the model update} \
        --image {path to input image}

Visualization

Figure 3. Visualization of reconstruction error with and without extra parameters using a pixel-style image as an example.

Figure 4. Comparisons with other instance adaptive codecs on out-of-domain images.

Citation

If you find this project useful for your research, please kindly cite our paper:

@inproceedings{lv2023dynamic,
  title={Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression},
  author={Lv, Yue and Xiang, Jinxi and Zhang, Jun and Yang, Wenming and Han, Xiao and Yang, Wei},
  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
  pages={632--642},
  year={2023}
}