Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China.
PLoS One. 2022 Mar 28;17(3):e0266021. doi: 10.1371/journal.pone.0266021. eCollection 2022.
The distortion measurement plays an important role in panoramic image processing. Most measurement algorithms judge the panoramic image quality by means of weighting the quality of the local areas. However, such a calculation fails to globally reflect the quality of the panoramic image. Therefore, the multi-region adjacent pixels correlation (MRAPC) is proposed as the efficient feature for no-reference panoramic images quality assessment in this paper. Specifically, from the perspective of the statistical characteristics, the differences of the adjacent pixels in panoramic image are proved to be highly related to the degree of distortion and independent of image content. Besides, the difference map has limited pixel value range, which can improve the efficiency of quality assessment. Based on these advantages, the proposed MRAPC feature collaborates with the support vector regression to globally predict the quality of panoramic images. Extensive experimental results show that the proposed no-reference panoramic image quality assessment algorithm achieves higher evaluation performance than the existing algorithms.
失真度测量在全景图像处理中起着重要作用。大多数测量算法通过加权局部区域的质量来判断全景图像的质量。然而，这种计算方法无法全局反映全景图像的质量。因此，本文提出了多区域相邻像素相关性（MRAPC）作为无参考全景图像质量评估的有效特征。具体来说，从统计特征的角度来看，证明了全景图像中相邻像素的差异与失真程度高度相关，并且与图像内容无关。此外，差分图的像素值范围有限，可以提高质量评估的效率。基于这些优势，所提出的 MRAPC 特征与支持向量回归相结合，可全局预测全景图像的质量。大量实验结果表明，所提出的无参考全景图像质量评估算法比现有算法具有更高的评估性能。
