NRVC:用于视频压缩的神经表示与隐含的多层次融合网络
Shangdong Liu1, Puming Cao1, Yujian Feng1
1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种用于视频压缩 (NRVC) 的新型神经表示方法,该方法使用隐式神经表示 (INR) 来实现更高效的模型. 与现有方法相比,NRVC提高了视频压缩性能和质量.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 视频压缩的端到端深度模型正在进步,但通常是复杂的和参数繁重的.
- 隐式神经表示 (INR) 提供了一个轻量级的替代方案,但在特征提取保真度方面面临限制.
- 现有的INR方法很难准确地适应视频所需的复杂映射功能.
研究的目的:
- 开发一种使用隐式神经表示的更有效,更轻量级的视频压缩方法.
- 为了解决当前基于INR的视频压缩技术中特征提取奇点的局限性.
- 为了提高基于神经网络的压缩中的视频的映射函数的拟合精度.
主要方法:
- 提出了一种用于视频压缩 (NRVC) 的神经表示方法,利用隐性多尺度融合网络.
- 集成的规范化剩余网络,以提高INR在装配目标功能的有效性.
- 引入了视频压缩多尺度表示 (MSRVC) 网络,以进行强大的特征提取.
- 开发了一个特征提取通道注意力 (FECA) 块,以捕获通道间特征交互.
主要成果:
- 与NeRV方法相比,NRVC显示解码的峰值信号对噪声比率 (PSNR) 在类似的比特每像素 (BPP) 中增加了2.16%.
- 拟议的NRVC方法在PSNR方面明显优于传统的高效视频编码 (HEVC) 标准.
- 多尺度融合和道注意力机制有效地提高了网络适应视频映射功能的能力.
结论:
- NRVC为现有的基于INR的视频压缩方法提供了优质的替代方案,平衡了模型效率与高性能.
- 多尺度特征和道注意力的集成显著提高了用于视频压缩的神经表示的能力.
- 这种方法为开发下一代高效,高质量的视频压缩技术提供了有希望的方向.
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