蒙面特征残留编码用于神经视频压缩
Chajin Shin1, Yonghwan Kim1, KwangPyo Choi2
1School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
条件掩盖特征剩余 (CMFR) 编码通过对特征而不是像素进行操作来改善神经视频压缩. 这种方法提高了效率,并为未来的框架更好地利用时间信息.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 视频压缩 视频压缩
背景情况:
- 当前的神经视频压缩方法预测和使用面具,但像素域残留仍然很重要.
- 现有的方法无法有效地利用重建的时间上下文进行后续的压缩.
研究的目的:
- 为了解决像素域残余和神经视频压缩中时间上下文利用的局限性.
- 引入一种改进的神经视频压缩框架,称为条件掩盖特征残余 (CMFR) 编码.
主要方法:
- 使用神经网络从目标和预测中提取特征.
- 通过从目标特征中减去掩盖的预测特征来实现CMFR编码.
- 引入了一个缩放特征融合 (SFF) 模块,以有效地删除条件信息,以及一个运动精炼器,以提高光学流质量.
主要成果:
- 与没有提出方法的基线模型相比,实现了11.76%的比特节省.
- 在所有HEVC测试序列中平均显示出显著的改进.
- 验证了CMFR编码,SFF模块和Motion Refiner的有效性.
结论:
- CMFR编码通过在功能领域运行,为神经视频压缩提供了更有效的方法.
- 拟议的SFF模块和Motion Refiner进一步提高了压缩效率和解码质量.
- 开发的方法代表了神经视频压缩技术的重大进步.
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