论文标题
正交性缺陷补偿改善频率选择性图像外推
Orthogonality Deficiency Compensation for Improved Frequency Selective Image Extrapolation
论文作者
论文摘要
本文描述了一种非常有效的图像信号外推算法。它可用于图像和视频通信中的各种应用程序，例如隐藏数据被传输错误或视频编码预测损坏的数据。外推是在有限数量的已知样品上进行的，并将信号扩展到这些样品之外。因此，来自已知样品的信号迭代投影到不同的基础函数上，以生成信号模型。由于基本函数在已知样品的面积上不是正交的，因此我们提出了一个新的扩展，即正交性缺乏补偿，以应对非正交性。使用此扩展，可以实现非常好的结构性外推结果以及平滑区域。该算法可提高PSNR高达2 dB，并且与迄今为止存在外推算法相比，隐藏块损失的视觉质量更好。
This paper describes a very efficient algorithm for image signal extrapolation. It can be used for various applications in image and video communication, e.g. the concealment of data corrupted by transmission errors or prediction in video coding. The extrapolation is performed on a limited number of known samples and extends the signal beyond these samples. Therefore the signal from the known samples is iteratively projected onto different basis functions in order to generate a model of the signal. As the basis functions are not orthogonal with respect to the area of the known samples we propose a new extension, the orthogonality deficiency compensation, to cope with the non-orthogonality. Using this extension, very good extrapolation results for structured as well as for smooth areas are achievable. This algorithm improves PSNR up to 2 dB and gives a better visual quality for concealment of block losses compared to extrapolation algorithms existent so far.
