论文标题
打开黑框
Opening the Black Box of Learned Image Coders
论文作者
论文摘要
与手工制作的图像编解码器相反，端到端学习的有损图像编码器（LIC）在速率延伸性能方面表现出了越来越高的优势。但是，它们主要被视为黑盒系统，其可解释性并未得到很好的研究。在本文中，我们表明LIC学习了一组基础函数，以转换其在潜在空间中紧凑的表示的输入图像，就像图像编码标准中使用的正交变换一样。我们的分析提供了见解，以帮助了解学到的图像编码人员如何工作，并可以使未来的设计和开发受益。
End-to-end learned lossy image coders (LICs), as opposed to hand-crafted image codecs, have shown increasing superiority in terms of the rate-distortion performance. However, they are mainly treated as black-box systems and their interpretability is not well studied. In this paper, we show that LICs learn a set of basis functions to transform input image for its compact representation in the latent space, as analogous to the orthogonal transforms used in image coding standards. Our analysis provides insights to help understand how learned image coders work and could benefit future design and development.
