Improving Multi-generation Robustness of Learned Image Compression

Litian Li, Zheng Yang, Yongqi Zhai, Jiayu Yang, Ronggang Wang

Abstract: Benefiting from flexible network designs and end-to-end joint optimization approach, learned image compression (LIC) has demonstrated excellent coding performance 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 image quality 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 BPG even after 50 times reencoding. Our code is available at https://github.com/leelitian/Multi-Generation-Robust-Coding.