基于深度学习的HEVC内部编码非线性变换

Kun-Min Yang, Dong Liu, Feng Wu
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引用次数: 6

摘要

在混合视频编码框架中,采用变换来利用输入信号内部的依赖性。本文提出了一种基于深度学习的非线性编码方法。具体来说,我们将方向信息纳入残差域。然后,设计了一个卷积神经网络模型,以实现比传统的离散余弦变换更好的去相关和能量压缩。这项工作有两个主要贡献。首先,我们提出使用内预测信号来降低残差中的方向性。其次,我们提出了一种新的损失函数来表征训练过程中变换的效率。为了评估所提出的变换的压缩性能,我们将其实现到高效视频编码参考软件中。实验结果表明,该方法可将自然视频的bd率降低1.79%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-Based Nonlinear Transform for HEVC Intra Coding
In the hybrid video coding framework, transform is adopted to exploit the dependency within the input signal. In this paper, we propose a deep learning-based nonlinear transform for intra coding. Specifically, we incorporate the directional information into the residual domain. Then, a convolutional neural network model is designed to achieve better decorrelation and energy compaction than the conventional discrete cosine transform. This work has two main contributions. First, we propose to use the intra prediction signal to reduce the directionality in the residual. Second, we present a novel loss function to characterize the efficiency of the transform during the training. To evaluate the compression performance of the proposed transform, we implement it into the High Efficiency Video Coding reference software. Experimental results demonstrate that the proposed method achieves up to 1.79% BD-rate reduction for natural videos.
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