Embodied Image Quality Assessment for Robotic Intelligence

• URL: http://arxiv.org/abs/2412.18774v2
• Date: Mon, 30 Dec 2024 14:54:57 GMT
• Title: Embodied Image Quality Assessment for Robotic Intelligence
• Authors: Jianbo Zhang, Chunyi Li, Liang Yuan, Guoquan Zheng, Jie Hao, Guangtao Zhai,
• Abstract summary: We first propose an embodied image quality assessment (EIQA) frameworks. We establish assessment metrics for input images based on the downstream tasks of robot. Experiments demonstrate that quality assessment of embodied images is different from that of humans.
• Score: 36.80460733311791
• License:
• Abstract: Image quality assessment (IQA) of user-generated content (UGC) is a critical technique for human quality of experience (QoE). However, for robot-generated content (RGC), will its image quality be consistent with the Moravec paradox and counter to human common sense? Human subjective scoring is more based on the attractiveness of the image. Embodied agent are required to interact and perceive in the environment, and finally perform specific tasks. Visual images as inputs directly influence downstream tasks. In this paper, we first propose an embodied image quality assessment (EIQA) frameworks. We establish assessment metrics for input images based on the downstream tasks of robot. In addition, we construct an Embodied Preference Database (EPD) containing 5,000 reference and distorted image annotations. The performance of mainstream IQA algorithms on EPD dataset is finally verified. The experiments demonstrate that quality assessment of embodied images is different from that of humans. We sincerely hope that the EPD can contribute to the development of embodied AI by focusing on image quality assessment. The benchmark is available at https://github.com/Jianbo-maker/EPD_benchmark.

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