Adaptive Patch Labeling and Multi-label Feature Selection for 360-Degree Image Quality Assessment

Assessing the quality of 360-degree images based on individual regions presents a challenging task. The lack of ground truth opinion scores (MOS) for specific regions makes it difficult to evaluate image quality accurately. Existing datasets only provide MOS for entire 360-degree images, which limits the granularity of assessment. To overcome this challenge, we propose a novel framework that employs adaptive patch labeling techniques. We leverage a set of 2D-IQA methods to generate quality score distributions for each patch in the 360-degree images. These distributions, combined with the available MOS, serve as labels for individual patches, providing a more comprehensive characterization of patch quality. Furthermore, we use these labels to adaptively select and refine deep neural features. By selectively choosing label-specific features, we enhance the accuracy and effectiveness of patch-based 360-degree image quality assessment. This approach allows us to focus on the most relevant and informative features for each patch, resulting in improved assessment performance. The experimental results on two benchmark datasets demonstrate that adaptive patch labeling and feature selection achieve accurate and reliable performances, thus advancing the field of 360-degree image quality assessment.