Fractional interpolation has been extensively utilized in a series of video coding standards to generate fractional precision prediction to remove the temporal redundancy in consecutive frames. In the traditional interpolation filter based methods, the fractional samples are interpolated through a linear combination of the neighboring integer samples. This method is simple yet unable to accurately characterize the nonstationary video signals. Recently, convolutional neural network has been utilized in the fractional interpolation and shows superior performance compared with the traditional methods. However, only the reconstruction of the reference frame is used as the infer information source. All the other information contained in the bitstream or generated during the encoding/decoding procedure denoted as the compression prior is not utilized at all. In this paper, we give the first trial to involve some compression priors into the CNN to improve the performance of a in-loop coding tool. Specifically, we propose a Compression Priors assisted Convolutional Neural Network (CPCNN) to further improve the fractional interpolation efficiency. In addition to the reconstructed component, we additionally utilize two other compression priors – the corresponding residual component and col-located high quality component to boost the performance. Specifically, the residual component that indicates the prediction efficiency and contains effective texture information is utilized as a complementary input to the reconstructed one. While the col-located component provides more useful high quality information to help the reconstruction get rid of the quality fluctuation. Furthermore, a special network structure is designed to learn powerful representations of these triple input components. Comprehensive experiments have been conducted to demonstrate the effectiveness of our proposed CPCNN. The experimental results show that compared to HEVC, our proposed CPCNN achieves on average of 5.3%, 2.8% and 1.9% BD-Rate savings under LDP, LDB and RA configurations, respectively.

中文翻译：

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分数阶插值的压缩先验辅助卷积神经网络

小数插值已在一系列视频编码标准中得到广泛利用，以生成小数精度预测以消除连续帧中的时间冗余。在基于传统插值滤波器的方法中，分数样本是通过相邻整数样本的线性组合进行插值的。该方法很简单，但是不能准确地表征非平稳视频信号。近年来，卷积神经网络已被用于分数插值，与传统方法相比，它具有优越的性能。然而，仅参考帧的重构被用作推断信息源。比特流中包含的所有其他信息或在编码/解码过程中生成的表示为压缩先验的其他信息都没有被利用。在本文中，我们进行了第一个试验，将一些压缩先验包括到CNN中，以提​​高循环编码工具的性能。具体来说，我们提出了一种压缩先验辅助卷积神经网络（CPCNN），以进一步提高分数内插效率。除了重建的组件外，我们还利用其他两个压缩先验–相应的残差组件和并置的高质量组件来提高性能。具体地，指示预测效率并包含有效纹理信息的残差分量被用作重建的残差分量的补充输入。虽然位于同一位置的组件提供了更多有用的高质量信息，以帮助重建摆脱质量波动。此外，还设计了一种特殊的网络结构来学习这些三重输入组件的强大表示形式。已经进行了全面的实验，以证明我们提出的CPCNN的有效性。实验结果表明，与HEVC相比，我们提出的CPCNN在LDP，LDB和RA配置下的BD-Rate节省分别平均达到5.3％，2.8％和1.9％。已经进行了全面的实验，以证明我们提出的CPCNN的有效性。实验结果表明，与HEVC相比，我们提出的CPCNN在LDP，LDB和RA配置下的BD-Rate节省分别平均达到5.3％，2.8％和1.9％。已经进行了全面的实验，以证明我们提出的CPCNN的有效性。实验结果表明，与HEVC相比，我们提出的CPCNN在LDP，LDB和RA配置下的BD-Rate节省分别平均达到5.3％，2.8％和1.9％。