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 degrades significantly. 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 principles of automatic control systems, 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 including rate-distortion curves on five public image compression datasets demonstrate that the proposed CIC outperforms eight 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.

翻译：学习型图像压缩（LIC）是当前最先进的方法。然而，LIC的测试图像与训练图像之间存在固有差异，这在一定程度上导致了性能下降。特别是对于样本外、分布外或域外的测试图像，LIC的性能会显著下降。传统的LIC是一种串行图像压缩（SIC）方法，它采用具有串行编码和解码单元的开环架构。然而，根据自动控制系统的原理，闭环架构有潜力提升LIC的动态和静态性能。因此，本文提出了一种具有闭环编码和解码单元的循环图像压缩（CIC）方法，以最小化测试图像与训练图像之间的差距，并提升LIC的能力。所提出的CIC建立了一个非线性环路方程，并通过泰勒级数展开证明了重建图像与原始图像之间的稳态误差接近于零。所提出的CIC方法具有后训练和即插即用特性，可以建立在任何现有的先进SIC方法之上。在五个公开图像压缩数据集上的率失真曲线等实验结果表明，所提出的CIC在重建能力上优于八种竞争性的最先进开源SIC算法。实验结果进一步表明，所提出的方法适用于具有暗背景、锐利边缘、高对比度、网格形状或复杂图案的样本外测试图像。