  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.
