  This paper investigates the efficacy of jointly optimizing content-specific
post-processing filters to adapt a human oriented video/image codec into a
codec suitable for machine vision tasks. By observing that artifacts produced
by video/image codecs are content-dependent, we propose a novel training
strategy based on competitive learning principles. This strategy assigns
training samples to filters dynamically, in a fuzzy manner, which further
optimizes the winning filter on the given sample. Inspired by simulated
annealing optimization techniques, we employ a softmax function with a
temperature variable as the weight allocation function to mitigate the effects
of random initialization. Our evaluation, conducted on a system utilizing
multiple post-processing filters within a Versatile Video Coding (VVC) codec
framework, demonstrates the superiority of content-specific filters trained
with our proposed strategies, specifically, when images are processed in
blocks. Using VVC reference software VTM 12.0 as the anchor, experiments on the
OpenImages dataset show an improvement in the BD-rate reduction from -41.3% and
-44.6% to -42.3% and -44.7% for object detection and instance segmentation
tasks, respectively, compared to independently trained filters. The statistics
of the filter usage align with our hypothesis and underscore the importance of
jointly optimizing filters for both content and reconstruction quality. Our
findings pave the way for further improving the performance of video/image
codecs.
