Versatile Video Coding based Coding Tree Unit Level Image Compression with Dual Quantization Parameters for Hybrid Vision

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...

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Bibliographic Details
Published inIEEE access Vol. 11; p. 1
Main Authors Kim, Shin, Lee, Yegi, Yoon, Kyoungro
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Codec
Contingency
High definition
Human performance
Hybrid Vision
Image analysis
Image coding
Image compression
Image manipulation
Image quality
Image resolution
Machine Vision
Measurement
Object recognition
Parameters
Versatile Video Coding
Video Coding for Machines
Video compression
Vision systems
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Summary: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 occasion 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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3263207