论文标题
改善学习图像压缩的多代鲁棒性
Improving Multi-generation Robustness of Learned Image Compression
论文作者
论文摘要
从灵活的网络设计和端到端的关节优化方法中受益，学习的图像压缩（LIC）近年来表现出了出色的编码性能和实际可行性。但是，现有的压缩模型遭受了严重的多生损失，这总是在图像编辑和转编码过程中发生。在反复编码和解码的过程中，图像的质量将迅速降低，从而导致各种失真，这显着限制了LIC的实际应用。在本文中，进行了彻底的分析，以确定连续图像压缩（SIC）中生成损失的来源。我们指出并解决了影响SIC的量化漂移问题，提出了可逆性损失函数以及通道松弛方法，以进一步减少产生损失。实验表明，通过使用我们提出的解决方案，LIC即使在重新编码50次而没有任何网络结构的更改后，LIC也可以实现与BPG的首次压缩的可比性能。
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.
