  We introduce COOL-CHIC, a Coordinate-based Low Complexity Hierarchical Image
Codec. It is a learned alternative to autoencoders with 629 parameters and 680
multiplications per decoded pixel. COOL-CHIC offers compression performance
close to modern conventional MPEG codecs such as HEVC and is competitive with
popular autoencoder-based systems. This method is inspired by Coordinate-based
Neural Representations, where an image is represented as a learned function
which maps pixel coordinates to RGB values. The parameters of the mapping
function are then sent using entropy coding. At the receiver side, the
compressed image is obtained by evaluating the mapping function for all pixel
coordinates. COOL-CHIC implementation is made open-source.
