Abstract
Multiview video plus depth (MVD) is a popular video format that supports three-dimensional television (3DTV) and free viewpoint television (FTV). 3DTV and FTV provide depth sensation to the viewer by presenting two views of the same scene but with slightly different angles. In MVD, few views are captured, and each view has the color image and the corresponding depth map which is used in depth image-based rendering (DIBR) to generate views at novel viewpoints. The DIBR can introduce various artifacts in the synthesized view resulting in poor quality. Therefore, evaluating the quality of the synthesized image is crucial to provide an appreciable quality of experience (QoE) to the viewer. In a 3D scene, objects are at a different distance from the camera, characterized by their depth. In this paper, we investigate the effect that objects at a different distance make on the overall QoE. In particular, we find that the quality of the closer objects contributes more to the overall quality as compared to the background objects. Based on this phenomenon, we propose a 3D quality assessment metric to evaluate the quality of the synthesized images. The proposed metric using the depth of the scene divides the image into different layers where each layer represents the objects at a different distance from the camera. The quality of each layer is individually computed, and their scores are pooled together to obtain a single quality score that represents the quality of the synthesized image. The performance of the proposed metric is evaluated on two benchmark DIBR image databases. The results show that the proposed metric is highly accurate and performs better than most existing 2D and 3D quality assessment algorithms.
Highlights
In everyday life, humans gain a lot of information through magazines, television, videos, images, etc. along with capturing, viewing, receiving, sending, and utilizing the information [1].With the already presence of two-dimensional (2D) technologies, different three-dimensional (3D) technologies have been introduced to the customers since the past few years mainly through cinemas, gaming, 3D televisions (3DTV) [2], and free viewpoint television (FTV) [3]
Motivated by the findings of this investigation, we propose a depth image-based rendering (DIBR)-synthesized image quality assessment metric that finds the salient regions in the image with the help of the corresponding depth map, and based on the saliency, each pixel or region contributes differently to the final quality of the image
The results show that the proposed metric outperforms all compared methods in Pearson’s linear correlation coefficient (PLCC) and achieves more than a Method peak signalto-noise ratio (PSNR) structural similarity index (SSIM) Multiscale structural similarity index (MSSIM) Visual signal-to-noise ratio (VSNR) Weighted signal-to-noise ratio (WSNR) Visual information fidelity (VIF) VIFP Information Fidelity Criterion (IFC) Universal Quality Index (UQI) 3D-Layered Quality Metric (3D-LQM)
Summary
In a 3D scene, objects are at a different distance from the camera, characterized by their depth. We find that the quality of the closer objects contributes more to the overall quality as compared to the background objects. Based on this phenomenon, we propose a 3D quality assessment metric to evaluate the quality of the synthesized images. The proposed metric using the depth of the scene divides the image into different layers where each layer represents the objects at a different distance from the camera.
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