  With the development of streaming media technology, increasing communication
relies on sound and visual information, which puts a massive burden on online
media. Data compression becomes increasingly important to reduce the volume of
data transmission and storage. To further improve the efficiency of image
compression, researchers utilize various image processing methods to compensate
for the limitations of conventional codecs and advanced learning-based
compression methods. Instead of modifying the image compression oriented
approaches, we propose a unified image compression preprocessing framework,
called Kuchen, which aims to further improve the performance of existing
codecs. The framework consists of a hybrid data labeling system along with a
learning-based backbone to simulate personalized preprocessing. As far as we
know, this is the first exploration of setting a unified preprocessing
benchmark in image compression tasks. Results demonstrate that the modern
codecs optimized by our unified preprocessing framework constantly improve the
efficiency of the state-of-the-art compression.
