Bitrate reduction with little to no degradation in visual perception is a long-standing challenge in video coding. This paper targets this challenge by adaptively filtering the content prior to video compression and in the preprocessing stage. This is done by applying a bilateral filter where the filter parameters are selected according to regional content complexity and estimated visual importance besides bitrate and quality requirements. A multi-scale metric based on 2D gradient is employed to determine bandwidth requirements of different regions. A random forest regression model is trained to predict distortion and bit requirements for a block, if it is filtered and encoded at a given quality. The predicted distortion and bit requirements are used to select filter parameters considering a cost function. The proposed approach is applied to both H.264 and HEVC encoders, with different GOP structures. The results show up to 60% bitrate reduction in terms of BD-Rate (about 20% on average) for the attempted test cases with little to no noticeable quality degradation.

中文翻译：

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用于视频压缩的内容自适应预过滤

在视觉感知几乎没有退化的情况下降低比特率是视频编码中长期存在的挑战。本文通过在视频压缩之前和预处理阶段自适应过滤内容来应对这一挑战。这是通过应用双边滤波器来完成的，其中除了比特率和质量要求之外，还根据区域内容复杂性和估计的视觉重要性来选择滤波器参数。采用基于 2D 梯度的多尺度度量来确定不同区域的带宽需求。随机森林回归模型被训练来预测块的失真和比特需求，如果它以给定的质量进行过滤和编码。预测的失真和比特要求用于考虑成本函数来选择滤波器参数。所提出的方法适用于具有不同 GOP 结构的 H.264 和 HEVC 编码器。结果显示，对于尝试的测试用例，就 BD-Rate 而言，比特率降低了 60%（平均约 20%），几乎没有或没有明显的质量下降。