Embedding Compression Distortion in Video Coding for Machines
Journal:
arXiv
Published Date:
Mar 27, 2025
Abstract
Currently, video transmission serves not only the Human Visual System (HVS)
for viewing but also machine perception for analysis. However, existing codecs
are primarily optimized for pixel-domain and HVS-perception metrics rather than
the needs of machine vision tasks. To address this issue, we propose a
Compression Distortion Representation Embedding (CDRE) framework, which
extracts machine-perception-related distortion representation and embeds it
into downstream models, addressing the information lost during compression and
improving task performance. Specifically, to better analyze the
machine-perception-related distortion, we design a compression-sensitive
extractor that identifies compression degradation in the feature domain. For
efficient transmission, a lightweight distortion codec is introduced to
compress the distortion information into a compact representation.
Subsequently, the representation is progressively embedded into the downstream
model, enabling it to be better informed about compression degradation and
enhancing performance. Experiments across various codecs and downstream tasks
demonstrate that our framework can effectively boost the rate-task performance
of existing codecs with minimal overhead in terms of bitrate, execution time,
and number of parameters. Our codes and supplementary materials are released in
https://github.com/Ws-Syx/CDRE/.
