# Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-driven Image Coding

arXiv: https://arxiv.org/abs/2301.08533 · HTML: https://mlsanity.com/paper/2301.08533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 8 of 8)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions](https://mlsanity.com/paper/1809.04098/) (2018)
- [High efficiency compression for object detection](https://mlsanity.com/paper/1710.11151/) (2017)
- [Video Coding for Machines with Feature-Based Rate-Distortion Optimization](https://mlsanity.com/paper/2203.05890/) (2022)
- [Saliency-Driven Versatile Video Coding for Neural Object Detection](https://mlsanity.com/paper/2203.05944/) (2022)
- [On Intra Video Coding and In-loop Filtering for Neural Object Detection Networks](https://mlsanity.com/paper/2203.05927/) (2022)

## Built on by (most cited first, 1 of 1)

- [NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines](https://mlsanity.com/paper/2401.10761/) (2024)

## Abstract (arXiv)

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.