Deep Video Codec Control for Vision Models
CoRR(2023)
摘要
Standardized lossy video coding is at the core of almost all real-world video
processing pipelines. Rate control is used to enable standard codecs to adapt
to different network bandwidth conditions or storage constraints. However,
standard video codecs (e.g., H.264) and their rate control modules aim to
minimize video distortion w.r.t human quality assessment. We demonstrate
empirically that standard-coded videos vastly deteriorate the performance of
deep vision models. To overcome the deterioration of vision performance, this
paper presents the first end-to-end learnable deep video codec control that
considers both bandwidth constraints and downstream deep vision performance,
while adhering to existing standardization. We demonstrate that our approach
better preserves downstream deep vision performance than traditional
approaches.
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关键词
control,video
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