  Recently, more and more images are compressed and sent to the back-end
devices for the machine analysis tasks~(\textit{e.g.,} object detection)
instead of being purely watched by humans. However, most traditional or learned
image codecs are designed to minimize the distortion of the human visual system
without considering the increased demand from machine vision systems. In this
work, we propose a preprocessing enhanced image compression method for machine
vision tasks to address this challenge. Instead of relying on the learned image
codecs for end-to-end optimization, our framework is built upon the traditional
non-differential codecs, which means it is standard compatible and can be
easily deployed in practical applications. Specifically, we propose a neural
preprocessing module before the encoder to maintain the useful semantic
information for the downstream tasks and suppress the irrelevant information
for bitrate saving. Furthermore, our neural preprocessing module is
quantization adaptive and can be used in different compression ratios. More
importantly, to jointly optimize the preprocessing module with the downstream
machine vision tasks, we introduce the proxy network for the traditional
non-differential codecs in the back-propagation stage. We provide extensive
experiments by evaluating our compression method for two representative
downstream tasks with different backbone networks. Experimental results show
our method achieves a better trade-off between the coding bitrate and the
performance of the downstream machine vision tasks by saving about 20% bitrate.
