Abstract
Depth-image-based rendering (DIBR) is widely used in 3DTV, free-viewpoint video, and interactive 3D graphics applications. Typically, synthetic images generated by DIBR-based systems incorporate various distortions, particularly geometric distortions induced by object dis-occlusion. Ensuring the quality of synthetic images is critical to maintaining adequate system service. However, traditional 2D image quality metrics are ineffective for evaluating synthetic images as they are not sensitive to geometric distortion. In this paper, we propose a novel no-reference image quality assessment method for synthetic images based on convolutional neural networks, introducing local image saliency as prediction weights. Due to the lack of existing training data, we construct a new DIBR synthetic image dataset as part of our contribution. Experiments were conducted on both the public benchmark IRCCyN/IVC DIBR image dataset and our own dataset. Results demonstrate that our proposed metric outperforms traditional 2D image quality metrics and state-of-the-art DIBR-related metrics.
Highlights
With the development of mobile devices and wirelessManuscript received: 2018-12-12; accepted: 2019-01-27image
In contrast to existing depth-image-based rendering (DIBR)-related metrics, which heavily rely on handcrafted features, we propose a no-reference (NR) DIBR synthetic image quality assessment method using convolutional neural networks (CNNs) and local image saliency based weighting
We provide the details of our experimental settings and give a performance comparison for our proposed DIBR synthetic image quality metric on the benchmark IRCCyN/IVC DIBR image dataset and our own dataset
Summary
Most are extensions of existing 2D IQA methods, assuming that DIBR synthetic images follow the same natural scene statistics (NSS) as traditional 2D images [6,7,8,9]. Their improvements mainly rely on carefully designed handcrafted features. In contrast to existing DIBR-related metrics, which heavily rely on handcrafted features, we propose a no-reference (NR) DIBR synthetic image quality assessment method using convolutional neural networks (CNNs) and local image saliency based weighting. We exploit the power of CNNs for synthetic image feature extraction, while utilizing the sensitivity of local image saliency to geometric distortions to refine the predicted scores.
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