CIC: Circular Image Compression
<|reference_start|>CIC: Circular Image Compression: Learned image compression (LIC) is currently the cutting-edge method. However, the inherent difference between testing and training images of LIC results in performance degradation to some extent. Especially for out-of-sample, out-of-distribution, or out-of-domain testing images, the performance of LIC dramatically degraded. Classical LIC is a serial image compression (SIC) approach that utilizes an open-loop architecture with serial encoding and decoding units. Nevertheless, according to the theory of automatic control, a closed-loop architecture holds the potential to improve the dynamic and static performance of LIC. Therefore, a circular image compression (CIC) approach with closed-loop encoding and decoding elements is proposed to minimize the gap between testing and training images and upgrade the capability of LIC. The proposed CIC establishes a nonlinear loop equation and proves that steady-state error between reconstructed and original images is close to zero by Taylor series expansion. The proposed CIC method possesses the property of Post-Training and plug-and-play which can be built on any existing advanced SIC methods. Experimental results on five public image compression datasets demonstrate that the proposed CIC outperforms five competing state-of-the-art open-source SIC algorithms in reconstruction capacity. Experimental results further show that the proposed method is suitable for out-of-sample testing images with dark backgrounds, sharp edges, high contrast, grid shapes, or complex patterns.<|reference_end|>
@article{li2024cic:,
    title={CIC: Circular Image Compression},
    author={Honggui Li, Sinan Chen, Nahid Md Lokman Hossain, Maria Trocan, Dimitri
  Galayko, Mohamad Sawan},
    journal={arXiv preprint arXiv:2407.15870},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15870},
    primaryClass={eess.IV cs.CV cs.LG}
}