16,192 research outputs found
Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-driven Image Coding
Today, visual data is often analyzed by a neural network without any human
being involved, which demands for specialized codecs. For standard-compliant
codec adaptations towards certain information sinks, HEVC or VVC provide the
possibility of frequency-specific quantization with scaling lists. This is a
well-known method for the human visual system, where scaling lists are derived
from psycho-visual models. In this work, we employ scaling lists when
performing VVC intra coding for neural networks as information sink. To this
end, we propose a novel data-driven method to obtain optimal scaling lists for
arbitrary neural networks. Experiments with Mask R-CNN as information sink
reveal that coding the Cityscapes dataset with the proposed scaling lists
result in peak bitrate savings of 8.9 % over VVC with constant quantization. By
that, our approach also outperforms scaling lists optimized for the human
visual system. The generated scaling lists can be found under
https://github.com/FAU-LMS/VCM_scaling_lists.Comment: Originally submitted at IEEE ICIP 202
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