Controlling Rate, Distortion, and Realism: Towards a Single Comprehensive Neural Image Compression Model
<|reference_start|>Controlling Rate, Distortion, and Realism: Towards a Single Comprehensive Neural Image Compression Model: In recent years, neural network-driven image compression (NIC) has gained significant attention. Some works adopt deep generative models such as GANs and diffusion models to enhance perceptual quality (realism). A critical obstacle of these generative NIC methods is that each model is optimized for a single bit rate. Consequently, multiple models are required to compress images to different bit rates, which is impractical for real-world applications. To tackle this issue, we propose a variable-rate generative NIC model. Specifically, we explore several discriminator designs tailored for the variable-rate approach and introduce a novel adversarial loss. Moreover, by incorporating the newly proposed multi-realism technique, our method allows the users to adjust the bit rate, distortion, and realism with a single model, achieving ultra-controllability. Unlike existing variable-rate generative NIC models, our method matches or surpasses the performance of state-of-the-art single-rate generative NIC models while covering a wide range of bit rates using just one model. Code will be available at https://github.com/iwa-shi/CRDR<|reference_end|>
@article{iwai2024controlling,
    title={Controlling Rate, Distortion, and Realism: Towards a Single
  Comprehensive Neural Image Compression Model},
    author={Shoma Iwai, Tomo Miyazaki, Shinichiro Omachi},
    journal={arXiv preprint arXiv:2405.16817},
    year={2024},
    doi={10.1109/WACV57701.2024.00288},
    archivePrefix={arXiv},
    eprint={2405.16817},
    primaryClass={cs.CV eess.IV}
}