Learning-based image compression methods have improved in recent years and started to outperform traditional codecs. However, neural-network approaches can unexpectedly introduce visual artifacts in some images. We therefore propose methods to separately detect three types of artifacts (texture and boundary degradation, color change, and text corruption), to localize the affected regions, and to quantify the artifact strength. We consider only those regions that exhibit distortion due solely to the neural compression but that a traditional codec recovers successfully at a comparable bitrate. We employed our methods to collect artifacts for the JPEG AI verification model with respect to HM-18.0, the H.265 reference software. We processed about 350,000 unique images from the Open Images dataset using different compression-quality parameters; the result is a dataset of 46,440 artifacts validated through crowd-sourced subjective assessment. Our proposed dataset and methods are valuable for testing neural-network-based image codecs, identifying bugs in these codecs, and enhancing their performance. We make source code of the methods and the dataset publicly available.

翻译：近年来，基于学习的图像压缩方法不断改进，并开始超越传统编解码器。然而，神经网络方法可能会在某些图像中意外引入视觉伪影。因此，我们提出了分别检测三种伪影类型（纹理与边界退化、颜色变化以及文本损坏）的方法，以定位受影响区域并量化伪影强度。我们仅考虑那些仅因神经压缩而出现失真、但传统编解码器在可比比特率下能成功恢复的区域。我们采用这些方法，针对JPEG AI验证模型相对于H.265参考软件HM-18.0收集了伪影数据。我们使用不同的压缩质量参数处理了来自Open Images数据集的约35万张独特图像；结果得到了一个包含46,440个经过众包主观评估验证的伪影数据集。我们提出的数据集和方法对于测试基于神经网络的图像编解码器、识别这些编解码器中的错误以及提升其性能具有重要价值。我们已公开提供方法的源代码及数据集。