Blind DIBR-synthesized view quality assessment by integrating local geometry and global structure analysis

The realization of free viewpoint videos (FVV) relies heavily on depth-image-based-rendering (DIBR) technology, but the imperfections of DIBR usually lead to local geometric distortions that significantly impact user experience. Therefore, it is crucial to develop a specialized image quality assessment (IQA) model for DIBR-synthesized views. To address this, this paper leverages local geometry and global structure analysis for DIBR-synthesized IQA (LGGS-SIQA). Specifically, in the local geometry-aware feature extraction module, the proposed method introduces an auxiliary task that converts the score learning task into a distortion classification task, aiming to simplify score sample expansion while effectively locating local geometric distortion regions. Based on this, different types of DIBR-synthesized distortions are further detected and weighted to obtain local geometric features. In the global structure-aware feature extraction module, as DIBR-synthesized distortions are mainly concentrated at object edges, the proposed method designs a strategy to extract key structures globally. Statistical analysis of these regions is performed to obtain robust global structural features. Finally, these two types of features are fused and regressed to obtain the final quality score. Experimental results on public benchmark databases show that the proposed LGGS-SIQA method outperforms existing manually extracted-based and deep learning-based IQA methods. Besides, feature ablation experiments validate the effectiveness of the core components of the proposed LGGS-SIQA method.