  Benefit from flexible network designs and end-to-end joint optimization
approach, learned image compression (LIC) has demonstrated excellent coding
performance and practical feasibility in recent years. However, existing
compression models suffer from serious multi-generation loss, which always
occurs during image editing and transcoding. During the process of repeatedly
encoding and decoding, the quality of the image will rapidly degrade, resulting
in various types of distortion, which significantly limits the practical
application of LIC. In this paper, a thorough analysis is carried out to
determine the source of generative loss in successive image compression (SIC).
We point out and solve the quantization drift problem that affects SIC,
reversibility loss function as well as channel relaxation method are proposed
to further reduce the generation loss. Experiments show that by using our
proposed solutions, LIC can achieve comparable performance to the first
compression of BPG even after 50 times reencoding without any change of the
network structure.
