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Sequential Error Concealment via Canonical Correlation Analysis

机译:通过规范相关性分析顺序错误隐藏

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In this paper, we propose a new error concealment algorithm for video/image based on canonical correlation analysis (CCA). Motivated by the Intra prediction in H.264/AVC, it is reasonable to assume that there is a strong spatial correlation relationship between the lost regions and their known adjacent regions. Based on the above idea, we use CCA to estimate a correlation projection matrix which utilizes the loss of macro block adjacent spatial information, then we use the projection matrix and the adjacent region to estimate missing pixel area. In addition, in order to use the spatial information efficiently, we apply the neighbor-embedding-type weight into the aforementioned CCA model. Experimental results demonstrate that the proposed method improves the subjective and objective image quality to a large extent in comparison with other existing methods.
机译:在本文中,我们提出了一种基于规范相关分析(CCA)的视频/图像的新误差隐藏算法。通过H.264 / AVC的帧内预测,​​假设丢失区域与其已知的相邻区域之间存在强的空间相关关系是合理的。基于上述思想,我们使用CCA来估计相关投影矩阵,该相关投影矩阵利用宏块相邻的空间信息的丢失,然后我们使用投影矩阵和相邻区域来估计缺失的像素区域。另外,为了有效地使用空间信息,我们将邻居嵌入式权重应用于前述CCA模型。实验结果表明,与其他现有方法相比,该方法在很大程度上提高了主观和客观图像质量。

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