Versatile Video Coding-Based Coding Tree Unit Level Image Compression With Dual Quantization Parameters for Hybrid Vision

Abstract

Image analysis based on machine vision is hugely manipulated in the smart industry. Good-quality images are required for outstanding machine analysis results, but handling high-definition images could be problematic in a constrained environment such as a low-bandwidth network or low-capacity storage. Lowering the image resolution might be a straightforward solution for reducing image data, but it would cause much information loss, leading to the deterioration of machine vision. Moreover, human supervision could be necessary for a contingency that machine vision cannot control. Therefore, an innovative image compression method considering machine and human vision is required; more compression efficiency than the state-of-the-art codec, praiseworthy machine vision performance, and human-recognizable quality. In this paper, we propose Versatile video coding(VVC) based image compression for hybrid vision, i.e., machine vision and human vision. Our work provides a coding tree unit(CTU) level image compression with dual quantization parameters (QPs) according to the quantization parameter map and the saliency extracted by the object detection network; in the salient region, the proposed method maintains high quality with low QP but degrades the quality with high QP in the non-salient region. Compared with VVC, the proposed compression method achieves a bitrate reduction of up to 25.5% in machine vision tasks, proving more compression efficiency and still admirable machine vision performance. From the perspective of human vision, the proposed method provides human-perceptible image quality, preserving acceptable objective quality values.

Cite this paper

@article{kim2023versatile,
  title = {{Versatile Video Coding-Based Coding Tree Unit Level Image Compression With Dual Quantization Parameters for Hybrid Vision}},
  author = {Kim, Shin and Lee, Yegi and Yoon, Kyoungro},
  journal = {IEEE Access},
  volume = {11},
  pages = {34498--34509},
  year = {2023},
  doi = {10.1109/access.2023.3263207}
}

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