论文标题
凉爽：基于坐标的低复杂性分层图像编解码器
COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec
论文作者
论文摘要
我们介绍了Cool-Chic，这是一种基于坐标的低复杂性分层图像编解码器。它是每次解码像素的629个参数和680个乘法的自动编码器的替代方法。 Cool-Chic提供接近现代传统MPEG编解码器（例如HEVC）的压缩性能，并且与流行的基于自动编码器的系统具有竞争力。该方法的灵感来自基于坐标的神经表示，其中图像表示为学习的函数，该函数将像素坐标映射到RGB值。然后使用熵编码发送映射函数的参数。在接收器侧，通过评估所有像素坐标的映射函数来获得压缩图像。酷炫的实施是开源的。
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.
