提出自适应锐化模型，平衡画质与码率，防止过度锐化。
Frequency-Assisted Adaptive Sharpening Scheme Considering Bitrate and Quality Tradeoff
- 基于高频特征和卷积神经网络预测最优锐化强度。
- 在保持画质的同时降低码率，避免过锐化问题。
- 适合视频编码、流媒体传输等需要画质-带宽平衡的场景。
锐化是提升视频质量的常用技术，能有效增强纹理并缓解模糊。然而，提高锐化程度会导致视频码率上升，从而影响服务质量（QoS）。此外，画质并不随锐化程度增加而持续改善，可能引发过锐化问题。因此，如何在控制带宽成本的同时选择合适的锐化水平以提升画质至关重要。本文提出一种新型频率辅助锐化等级预测模型（FreqSP）。首先，为每段视频标注对应最佳码率与画质权衡下的锐化等级作为真实标签；随后，以未压缩源视频为输入，利用复杂的卷积神经网络特征与高频分量估计最优锐化等级。大量实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Sharpening is a widely adopted technique to improve video quality, which can effectively emphasize textures and alleviate blurring. However, increasing the sharpening level comes with a higher video bitrate, resulting in degraded Quality of Service (QoS). Furthermore, the video quality does not necessarily improve with increasing sharpening levels, leading to issues such as over-sharpening. Clearly, it is essential to figure out how to boost video quality with a proper sharpening level while also controlling bandwidth costs effectively. This paper thus proposes a novel Frequency-assisted Sharpening level Prediction model (FreqSP). We first label each video with the sharpening level correlating to the optimal bitrate and quality tradeoff as ground truth. Then taking uncompressed source videos as inputs, the proposed FreqSP leverages intricate CNN features and high-frequency components to estimate the optimal sharpening level. Extensive experiments demonstrate the effectiveness of our method.
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