No-Reference High Dynamic Range Omnidirectional Image Quality Metric: From the Perspective of Global and Local Statistical Characteristics

Abstract

High dynamic range omnidirectional image (HOI) can provide more real and immersive watching experience for viewers, thus has become an important presentation of virtual reality technology. However, both the system processing and the characteristics of HOI make the design of HOI quality metric (HOIQM) a challenging issue. In this work, considering the difference between whole field of view (FoV) and viewer-selected viewport, distortion features from both global and local perspectives are extracted, and a blind HOIQM is proposed. Specifically, because different regions have different projections in SSP projection, we have constructed the optimal bivariate response pair in the equatorial region and bipolar region according to their projection direction, and parameters in the BGGD based-spatial oriented correlation model are extracted as global statistical features. Meanwhile, combined with the visual perception for HOI, the key blocks are determined in equatorial region, and the local statistical characteristics of the key blocks are extracted by analyzing the distribution of multiscale structure information. Finally, the global and local features are regressed by SVR to obtain the final HOI quality. Experimental results on NBU-HOID database demonstrate that the proposed quality metric is outperformed the existing representative quality metrics and is more consistent with human visual perception for HOI.