Technology Category
Computer Vision: 3D Computer VisionMachine Learning: Calibration & Uncertainty QuantificationKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
Max360IQ: Blind omnidirectional image quality assessment with multi-axis attention
🤖 AI Summary
Non-uniform distortion modeling and severe equi-rectangular projection (ECP) geometric distortion hinder blind no-reference quality assessment of 360° panoramic images. Method: We propose the first end-to-end blind 360° IQA model, incorporating a multi-axis attention mechanism to jointly encode spherical geometry, cross-latitudinal semantic dependencies, and local-global distortion responses; integrating spherical coordinate embedding and hierarchical distortion-sensitive feature extraction; and enabling the first adaptive perception of ECP-induced distortions—without requiring distortion priors or full-reference images. Contribution/Results: The model performs direct regression of subjective quality scores. Evaluated on OVQ and SIAT-360IQ benchmarks, it achieves state-of-the-art performance with PLCC ≥ 0.92, significantly outperforming both leading 2D IQA methods and existing 360° IQA approaches.
Technology Category
Application Category
Problem
Research questions and friction points this paper is trying to address.
Assessing quality of omnidirectional images
Handling non-uniform image distortions
Improving user experience with 360-degree images
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-axis attention for spatial interaction
Multi-scale feature integration module
Deep semantic guidance for quality prediction
🔎 Similar Papers
No similar papers found.
