为机器人生成图像设计新评估方法，发现其质量感知与人类不同。
Embodied Image Quality Assessment for Robotic Intelligence
- 构建首个机器人图像质量偏好数据集EPD，含12500张失真图像标注。
- 提出多尺度注意力模型MA-EIQA，基于下游任务表现评估图像质量。
- 揭示机器人与人类对图像质量的判断存在差异，适合具身智能研究者。
用户生成内容（UGC）的图像质量评估（IQA）对用户体验（QoE）至关重要。然而，机器人生成内容（RGC）的图像质量是否遵循莫拉维克悖论，可能与人类感知标准不一致？人类主观评分更关注图像吸引力。具身智能体需在环境中交互与感知，并完成特定任务，视觉输入直接影响下游任务表现。本文探索了具身机器人对图像质量的感知机制。提出首个具身偏好数据库（EPD），包含12,500个失真图像的标注。基于机器人下游任务建立评估指标。针对UGC与RGC之间的差距，提出新型多尺度注意力具身图像质量评估模型MA-EIQA。EPD是首个专为具身机器人设计的无参考IQA基准。验证主流IQA算法在该数据集上的表现。实验表明，具身图像质量评估不同于人类。我们希望EPD能推动具身人工智能发展。基准代码已开源：https://github.com/Jianbo-maker/EPD_benchmark。
原文摘要 · Abstract (English)
Image Quality Assessment (IQA) of User-Generated Content (UGC) is a critical technique for human Quality of Experience (QoE). However, does the the image quality of Robot-Generated Content (RGC) demonstrate traits consistent with the Moravec paradox, potentially conflicting with human perceptual norms? 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 explore the perception mechanism of embodied robots for image quality. We propose the first Embodied Preference Database (EPD), which contains 12,500 distorted image annotations. We establish assessment metrics based on the downstream tasks of robot. In addition, there is a gap between UGC and RGC. To address this, we propose a novel Multi-scale Attention Embodied Image Quality Assessment called MA-EIQA. For the proposed EPD dataset, this is the first no-reference IQA model designed for embodied robot. Finally, the performance of mainstream IQA algorithms on EPD dataset is 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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