Visual Perception‐Based Quality Assessment for Stitched Panoramic Images

ABSTRACT Panoramic image stitching algorithms inevitably introduce distortions due to their inherent limitations. Due to the characteristics of the Human Visual System (HVS), slight stitching distortions have a minimal impact on perceived image quality, whereas severe distortions significantly degrade it. To address this issue, a visual perception‐based quality assessment method for stitched panoramic images is proposed. Existing no‐reference image quality assessment (NR‐IQA) methods often struggle to accurately model the HVS and its subjective perception of stitching distortions, leading to significant discrepancies between predicted and actual quality scores. Therefore, we use the learnt perceptual image patch similarity (LPIPS) to capture perceptual similarity from the HVS, thereby generating a high‐quality pseudo‐reference image (PRI). To further improve its perceptual quality, the PRI is refined using a visual restoration network (VRN) to reduce residual distortions. Subsequently, both the pseudo‐reference and distorted images are fed into a quality assessment network with shared parameters to extract deep feature representations. To improve distortion‐aware quality evaluation, we design an Adaptive Feature Fusion (AFF) module, where features from the pseudo‐reference and distorted images are first enhanced through an attention mechanism and then adaptively fused via learnt weights for more effective quality assessment. Finally, the fused features are processed through fully connected layers for regression, predicting the perceptual quality score of the stitched panoramic image. Extensive experimental results demonstrate that the proposed method outperforms state‐of‐the‐art approaches, achieving significant improvements across multiple evaluation metrics.