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.

翻译：从灵活的网络设计和端至端联合优化方法中受益的灵活网络设计和端至端联合优化方法、学习的图像压缩(LIC)近年来表现出极好的编码性能和实际可行性,然而,现有的压缩模型遭受了严重的多代损失,这在图像编辑和转换过程中总是发生。在反复编码和解码过程中,图像的质量会迅速下降,导致各种扭曲,从而大大限制了LIC的实际应用。在本文件中,进行了透彻的分析,以确定连续图像压缩(SIC)中遗传损失的来源。我们指出并解决了影响SIC、可逆性损失功能以及频道放松方法的量化漂移问题,以进一步减少生成损失。实验表明,通过使用我们提议的解决方案,LIC即使在50次重新编码之后,甚至在网络结构没有任何改变的情况下,仍可以实现与BPG的第一次压缩的类似性能。