通过基于波段的残余神经网络进行λ-域速率控制,用于VVC HDR内部编码
概括
本文介绍了一种用于多功能视频编码 (VVC) 高动态范围 (HDR) 内部的新速率控制算法. 数据驱动的方法提高了HDR视频压缩效率和视觉质量.
科学领域:
- 视频压缩方法 视频压缩
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 高动态范围 (HDR) 视频提供了增强的现实主义,但也带来了压缩挑战.
- 现有的多功能视频编码 (VVC) 速率控制算法针对标准动态范围 (SDR) 进行了优化,在HDR内容上表现不佳.
- 有效的速率控制对于高效的HDR视频交付至关重要.
研究的目的:
- 开发一个数据驱动的速度控制算法,用于VVC HDR内.
- 为了解决当前VVC频率控制对HDR视频的局限性.
- 为了提高HDR视频压缩的编码效率和视觉质量.
主要方法:
- 对HDR内部编码特征的分析.
- 为HDR内开发一个零碎的Rate-Lagrange参数 (R-λ) 模型.
- 基于波段的残余神经网络 (WRNN) 的实施,以优化CTU级位分配.
- 创建一个大规模的HDR数据集,用于训练WRNN.
主要成果:
- 拟议的零碎R-λ模型准确地捕捉了HDR内部的速率-扭曲关系.
- WRNN有效地预测R-λ模型参数,以优化位分配.
- 实验结果表明,与最先进的方法相比,编码性能优越.
- 开发的算法实现了更好的压缩效率VVC HDR内编码.
结论:
- 拟议的数据驱动的λ域速率控制算法显著增强了VVC HDR内编码.
- 基于WRNN的方法为HDR视频压缩中的深度学习应用提供了一个有希望的方向.
- 该算法为优化HDR视频交付提供了强大的解决方案.
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