Unifying Structural and Semantic Similarities for Quality Assessment of DIBR-Synthesized Views

Multi-view 3D content is subject to distortions during the process of depth image-based rendering (DIBR). Studies have shown the unreliable performance of the well-established image quality assessment (IQA) models for evaluation of DIBR-synthesized views which surge the need for more effective IQA methods. Existing objective methods generally rely on the pixel-wise correspondences between the reference and distorted images, while view synthesis can introduce pixel shifts. DIBR distortions such as stretching and local hole-filling errors have different visual impacts from conventional distortions, challenging the existing IQA models. Here, we developed a Full-Reference (FR) objective IQA metric for synthesized views that significantly outperforms 2D IQA and the state-of-the-art DIBR IQA approaches. While the pixel misalignment between the reference and synthesized views is a big challenge for quality assessment, we deployed a Convolutional Neural Network (CNN) model to acquire a feature representation that inherently offers resilience to the imperceptible pixel shift between the compared images. Therefore, our model does not need accurate shift compensation. We deployed a set of quality-aware CNN features representing high-order statistics, to measure the structural similarity which is combined with a semantic similarity measure for accurate quality assessment. Moreover, prediction accuracy is improved by incorporating a visual saliency model acquired using the activations of the higher CNN layers. Experimental results indicate a significant performance gain (14.6% in terms of Spearman's rank-order correlation) compared to the top existing IQA model. The source code of the proposed metric is available at: https://gitlab.com/saeedmp/sequss.