This study proposes a framework of video coding based on Laplacian eigenmaps (LEM) and its related embedding and reconstruction algorithm (ERA). Firstly, a one-dimensional (1D) representation of LEM is adopted to achieve an extremely low bit per pixel (BPP). Secondly, dual k -nearest neighbours, which keeps neighbour relationships both in high-dimensional data space and low-dimensional representation space and overcomes the disadvantage of classical non-linear dimensionality reduction methods which cannot preserve the neighbour properties in both of the spaces, based ERA of LEM is employed to gain extraordinarily high peak-signal-to-noise ratio (PSNR). Thirdly, a unified framework of video coding is fit for intra-frame, inter-frame and multi-view video coding. Finally, it is evaluated by simulation experiments that, in the situation of low bitrate transmission, the proposed method can attain better performance of BPP and PSNR than that of the state-of-the-art methods, such as highly efficient video coding.

中文翻译：

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拉普拉斯特征图和对偶的一维表示 ķ最近邻居进行统一视频编码

本研究提出了一种基于拉普拉斯特征图（LEM）及其相关嵌入和重建算法（ERA）的视频编码框架。首先，采用LEM的一维（1D）表示以实现极低的每像素位（BPP）。其次，双重ķ 最近邻算法，它在高维数据空间和低维表示空间中都保持邻居关系，并克服了经典非线性降维方法的缺点，该方法无法在两个空间中保留邻居属性，因此基于LEM的ERA用于获得非常高的峰值信噪比（PSNR）。第三，视频编码的统一框架适合于帧内，帧间和多视图视频编码。最后，通过仿真实验评估，在低比特率传输的情况下，与高效视频编码等最新技术相比，该方法具有更好的BPP和PSNR性能。