摘要
针对DCT方法用单一变换核处理所有图像块而忽略图像信号的复杂统计分布的问题,通过论证视频中的图像数据和运动预测残差存在的多线性子空间分布特性,提出一种可伸缩性视频编码方法.该方法用广义主成分分析(GPCA)取代传统视频编码中所采用的DCT来对I帧和预测残差图像编码,通过对编码结果进行适当排序,使得码流可以在任意点被截断,实现精细粒度的质量可伸缩性;并借助多线性子空间的分割,实现依据人类视觉注意特性的差错保护及更好的错误隐藏.对文中方法和基于DCT的可伸缩性编码效果进行比较的结果表明,在同等压缩比的情况下,采用该方法普遍可获得比DCT更好的图像质量.
The DCT method handles each image block with the same transform kernel, which ignores the complex distribution of image signals. To overcome this problem, this paper proposes a novel scalable coding method by demonstrating that video images and prediction residuals can be fitted with a multi-linear subspaee model. This method encodes I frames and prediction errors with generalized principle component analysis （GPCA） instead of traditional DCT. By appropriate reordering of GPCA coefficients, the generated scalable data stream can be truncated at random positions. An unequal error protection method taking advantage of the human visual attention model as well as a better error concealment method can be applied with the support of multiple subspaces. Experiments show that with the same amount of data, the proposed scalable video coding scheme can achieve better reconstructed video quality than the DCT-based method.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2010年第2期318-326,共9页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(60533090
60603096)
长江学者和创新团队发展计划(IRT0652)
关键词
可伸缩视频编码
广义主成分分析
多线性子空间模型
scalable video coding
generalized principle component analysis
multi-linear subspace model
