视频插值与多对多的裂纹和空间选择性精细化
IEEE transactions on pattern analysis and machine intelligence
|October 24, 2023
概括
本研究介绍了一种M2M (Many-to-Many) 分裂框架,用于高效的视频插入. 具有空间选择性精制 (SSR) 的M2M++模型通过选择性精制困难区域来提高插值质量,提高效率.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 视频插值对于提高视频质量和创建平滑运动至关重要.
- 现有的方法经常在效率和准确性方面扎,特别是在复杂的运动方面.
- 强度域对直接的像素扭曲和融合提出了挑战.
研究的目的:
- 开发一种新,高效,准确的视频插值框架.
- 为强大的像素合成引入一个多对多 (M2M) 喷方法.
- 通过选择性精炼机制提高插值质量.
主要方法:
- 提出了一个完全可差异化的M2M (Many-to-Many) 分割框架,利用双向流量估计.
- 像素是向前扭曲的目标时间步骤,与重叠的像素融合.
- 一个M2M++框架包含一个空间选择性精炼 (SSR) 组件,用于基于错误图的有针对性的精炼.
主要成果:
- 在M2M框架实现快速的多互插,最小的计算开销.
- 带有SSR的M2M++通过将计算集中在具有挑战性的区域上来证明了更好的插值准确性.
- 该方法在计算效率和插值质量之间提供可调节的权衡.
结论:
- 拟议的M2M分片框架为视频插入提供了一个有效的解决方案.
- 使用SSR的M2M++扩展显著提高了插值准确性和灵活性.
- 这种方法提供了具有竞争力的视频插值质量,具有可适应的计算需求.
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