Recently, the popularity of 3D content is on the rise because of its immersive experience to view- ers. While demands for 3D contents and 3D technologies increase, only a few copyright protection methods for 3D conten...Recently, the popularity of 3D content is on the rise because of its immersive experience to view- ers. While demands for 3D contents and 3D technologies increase, only a few copyright protection methods for 3D contents have been proposed. The simplest infringement is the illegal distribution of the single 2D image from 3D content. The leaked image is still valuable as 2D content and the leakage can be occurred in DIBR system. To detect the leaked image, we focus on the hole-filled region which is caused by the hole-filling procedure mandatory in DIBR system. To estimate the hole-filled regions, two different procedures are conducted to extract edges and to estimate 3D warping traces, respectively. After that, the hole-filled regions are estimated and the left-right-eye image discrimination (LR discrimination) is also conducted. Experimental results demonstrate the effectiveness of the proposed method using quantitative measures.展开更多
Depth-image-based rendering(DIBR) is widely used in 3 DTV, free-viewpoint video, and interactive 3 D graphics applications. Typically, synthetic images generated by DIBR-based systems incorporate various distortions, ...Depth-image-based rendering(DIBR) is widely used in 3 DTV, free-viewpoint video, and interactive 3 D 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 2 D 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 2 D image quality metrics and state-of-the-art DIBR-related metrics.展开更多
文摘Recently, the popularity of 3D content is on the rise because of its immersive experience to view- ers. While demands for 3D contents and 3D technologies increase, only a few copyright protection methods for 3D contents have been proposed. The simplest infringement is the illegal distribution of the single 2D image from 3D content. The leaked image is still valuable as 2D content and the leakage can be occurred in DIBR system. To detect the leaked image, we focus on the hole-filled region which is caused by the hole-filling procedure mandatory in DIBR system. To estimate the hole-filled regions, two different procedures are conducted to extract edges and to estimate 3D warping traces, respectively. After that, the hole-filled regions are estimated and the left-right-eye image discrimination (LR discrimination) is also conducted. Experimental results demonstrate the effectiveness of the proposed method using quantitative measures.
基金sponsored by the National Key R&D Program of China (No. 2017YFB1002702)the National Natural Science Foundation of China (Nos. 61572058, 61472363)
文摘Depth-image-based rendering(DIBR) is widely used in 3 DTV, free-viewpoint video, and interactive 3 D 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 2 D 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 2 D image quality metrics and state-of-the-art DIBR-related metrics.
