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Synthesized View Comparison Method for No-Reference 3D Image Quality Assessment

机译：无参考3D图像质量评估的综合视图比较方法

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摘要

We develop a no-reference image quality assessment metric to evaluate the quality of synthesized view rendered from the Multi-view Video plus Depth (MVD) format. Our metric is named Synthesized View Comparison (SVC), which is designed for real-time quality monitoring at the receiver side in a 3D-TV system. The metric utilizes the virtual views in the middle which are warped from left and right views by Depth-image-based rendering algorithm (DIBR), and compares the difference between the virtual views rendered from different cameras by Structural SIMilarity (SSIM), a popular 2D full-reference image quality assessment metric. The experimental results indicate that our no-reference quality assessment metric for the synthesized images has competitive prediction performance compared with some classic full-reference image quality assessment metrics.
机译：我们开发了无参考图像质量评估指标，以评估从多视图视频加深度（MVD）格式呈现的合成视图的质量。我们的指标称为“综合视图比较（SVC）”，旨在在3D-TV系统中的接收器端进行实时质量监视。该指标利用中间深度的虚拟视图，这些虚拟视图通过基于深度图像的渲染算法（DIBR）从左视图和右视图扭曲，并比较通过流行的结构相似性（SSIM）从不同摄像机渲染的虚拟视图之间的差异。 2D全参考图像质量评估指标。实验结果表明，与一些经典的全参考图像质量评估指标相比，我们的合成图像无参考质量评估指标具有竞争优势。

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