Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Application
Just Recognizable Difference (JRD) boosts coding efficiency for machine vision through visibility threshold modeling, but is currently limited to a single-task scenario. To address this issue, we propose a Multi-Task JRD (MT-JRD) dataset and an Attribute-assisted MT-JRD (AMT-JRD) model for Video Coding for Machines (VCM), enhancing both prediction accuracy and coding efficiency. First, we construct a dataset comprising 27,264 JRD annotations from machines, supporting three representative tasks including object detection, instance segmentation, and keypoint detection. Secondly, we propose the AMT-JRD prediction model, which integrates Generalized Feature Extraction Module (GFEM) and Specia...
| Authors | Junqi Liu, Yun Zhang, Xiaoxia Huang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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