Adaptive Post Filter for Reducing Block Artifacts in High Efficiency Video Coding
Adaptive Post Filter for Reducing Block Artifacts in High Efficiency Video Coding
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DOI：
10.1109/icmssp.2016.014
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发表时间：
2016-09
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影响因子：
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通讯作者：
A. Chauvet;Tomo Miyazaki;Yoshihiro Sugaya;S. Omachi
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文献类型：
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作者：
A. Chauvet;Tomo Miyazaki;Yoshihiro Sugaya;S. Omachi
本文描述了一种用于H.265高效视频编码（HEVC）的基于神经网络的自适应去块后置滤波器。块噪声是视频编码中的常见问题，其由将帧划分为块而引起。滤波器是自适应的，因为它使用不同的滤波器参数取决于块特性。我们使用修改的HEVC解码器将块信息导出到滤波器。根据变换和预测参数将块放入不同的类别中。我们在每种情况下训练过滤器以优化内部参数。我们发现，2层卷积神经网络能够以适度的处理成本优于环路去块滤波器。我们提出在HEVC环路内去块滤波器之后应用滤波器。我们证明，我们的过滤器有助于进一步减少视频中的视觉伪影。
This paper describes an adaptive deblocking postfilter based on neural networks for use in H.265 High Efficiency Video Coding (HEVC). Blocking noise is a common problem in video coding caused by the division of the frame into blocks. The filter is adaptive because it uses different filter parameters depending on block characteristics. We use a modified HEVC decoder to export the block information to the filter. Blocks are put in different categories according to transform and prediction parameters. We train the filter in each case to optimize the internal parameters. We find that a 2-layer convolutional neural network is able to outperform the in-loop deblocking filter for a moderate processing cost. We propose to apply the filter after the HEVC in-loop deblocking filter. We demonstrate that our filter helps further reduce visual artifacts in video.