Spherical triangle mesh representation and multi-channel residual graph convolution network-based blind omnidirectional image quality assessment

Abstract

Compared with traditional 2D imaging, omnidirectional imaging techniques can provide users with 360°×180° immersive visual experience, which also make the objective quality assessment of omnidirectional images more challenging. In this work, a spherical triangle mesh representation and multi-channel residual graph convolution network (denoted as Multi-RES-GCN) based blind omnidirectional image quality assessment (IQA) is proposed. The proposed method includes two important stages: omnidirectional image’s spherical triangle mesh generation and optimization, and quality predictor based on Multi-RES-GCN. In the first stage, the spherical representation of omnidirectional image is used (called as spherical image), a new scheme of spherical triangle mesh generation and optimization is proposed, which can reasonably sample pixels on the spherical image and optimize the sampled points to generate more accurate triangular meshes. In the second stage, the spherical image is divided into six view regions, and the triangle mesh nodes are classified into the view regions according to their positions, and then input to the quality predictor. The quality predictor is composed of Multi-Res-GCN and Estimator. Multi-Res-GCN can model nodes and the dependency relationship between nodes. Estimator is designed to regress the features extracted by Multi-Res-GCN to the weights and quality score of each view region, and the final quality score of omnidirectional image is predicated by calculating the weighted summation of these quality scores. Experimental results demonstrate that the proposed method outperforms other state-of-the-art IQA metrics on two omnidirectional IQA databases.