Machine-oriented Visual Media Quality Assessment

Chunyi Li, Zicheng Zhang, Wei Zhou, Guangtao Zhai, Weisi Lin, Roger Zimmermann

Keywords: Multimedia Signal Processing, Image Quality Assessment, Embodied AI, Vision-Language-Action Model

Abstract: With the development of Embodied AI, machines have replaced humans as the main consumers of visual media, yet existing Image Quality Assessment (IQA) metrics remain focused on human and overlooked machine preference (a domain where quality depends on task utility rather than perceptual fidelity alone). To address this gap, we introduce the Machine-oriented Image Quality Assessment (MoIQA) Challenge, emphasizing simulation comprehension and Real-world exectution. The challenge comprises two tasks: 1) MoIQA-Sim, primarily evaluating the consistency between IQA models and the performance of mainstream Vision-Language Models (VLMs) in simulation software; and 2) MoIQA-Real, focusing on whether IQA models match the results of the latest Vision-Language-Action Models (VLAs) in the Real-world. This challenge aims to advance reliable image understanding for machine preference and support robust Embodied AI applications.

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Submission Number: 17