In the three-dimensional video system, the depth image-based rendering is a key technique for generating synthesized views, which provides audiences with depth perception and interactivity. However, the inaccuracy of depth information leads to geometrical rendering position errors, and the compression distortion of texture and depth videos degrades the quality of the synthesized views. Although existing quality enhancement methods can eliminate the distortions in the synthesized views, their huge computational complexity hinders their applications in real-time multimedia systems. To this end, a residual distillation enhanced network (RDEN)-guided lightweight synthesized view quality enhancement (SVQE) method is proposed to minimize holes and compression distortions in the synthesized views while reducing the model complexity. First, a rethinking on the deep-learning-based SVQE methods is performed. Then, a feature distillation attention block is proposed to effectively reduce the distortions in the synthesized views and make the model fulfill more real-time tasks, which is a lightweight and flexible feature extraction block using an information distillation mechanism and a lightweight multi-scale spatial attention mechanism. Third, a residual feature fusion block is proposed to improve the enhancement performance by using the feature fusion mechanism, which efficiently improves the feature extraction capability without introducing any additional parameters. Experimental results prove that the proposed RDEN efficiently improves the SVQE performance while consuming few computational complexities compared with the state-of-the-art SVQE methods.

中文翻译：

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RDEN：残余蒸馏增强型网络引导轻量级合成视图质量增强，适用于 3D-HEVC


在三维视频系统中，基于深度图像的渲染是生成合成视图的关键技术，为观众提供深度感知和交互性。然而，深度信​​息的不准确会导致几何渲染位置错误，并且纹理和深度视频的压缩失真会降低合成视图的质量。尽管现有的质量增强方法可以消除合成视图中的失真，但其巨大的计算复杂性阻碍了它们在实时多媒体系统中的应用。为此，提出了一种残差蒸馏增强网络（RDEN）引导的轻量级合成视图质量增强（SVQE）方法，以最小化合成视图中的空洞和压缩失真，同时降低模型复杂度。首先，对基于深度学习的SVQE方法进行了重新思考。然后，提出了特征蒸馏注意块，以有效减少合成视图中的失真并使模型完成更多实时任务，这是一种使用信息蒸馏机制和轻量级多尺度空间的轻量级且灵活的特征提取块注意机制。第三，提出了一种残差特征融合块，利用特征融合机制来提高增强性能，在不引入任何额外参数的情况下有效地提高了特征提取能力。实验结果证明，与最先进的 SVQE 方法相比，所提出的 RDEN 有效提高了 SVQE 性能，同时消耗很少的计算复杂性。