  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 Talor 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 open-source state-of-the-art
competing 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.
