Self Patch Labeling Using Quality Distribution Estimation for CNN-Based 360-IQA Training

In this study, we propose a methodology for estimating quality score distribution (QSD) for 360-IQA patch labeling. A collection of 2D-IQA models is used to generate a QSD for patches, inspired by how subjective quality ratings are gathered and handled. The proposed framework is first benchmarked on a subjectively annotated dataset, namely KonPatch-32k, in terms of patch quality classification. The best composition of QSD is then used to derive quality labels for patches sampled from 360-degree images. Furthermore, the quality labels are used in a multi-regression training strategy of CNN models. The ResNet-50 and EfficientNet-B5 are used to test the effectiveness of the proposed labeling framework on two publicly available 360-IQA datasets, namely OIQA and MVAQD. The experimental results demonstrated the efficacy of jointly using local and global qualities. The multi-regression proved to be a bit challenging on OIQA compared to MVAQD, reflecting the necessity to accurately regulate the training process.