QQ客服 官方微博 反馈留言

	Multi-scale spatial error concealment via hybrid Bayesian regression
	Liu, Xianming ; Zhai, Deming ; Zhai, Guangtao ; Zhao, Debin ; Xiong, Ruiqin ; Gao, Wen
	2012
英文摘要	In this paper, we propose a novel multi-scale spatial error concealment algorithm to combine the modeling strengthes of the parametric and nonparametric Bayesian regression. We progressively recover missing blocks in the scale space from coarse to fine so that the sharp edges and texture in the finest scale can be eventually recovered. On one hand, in each scale, the nonparametric part of the methodology is used to exploit the intra-scale correlation, which relies on the data itself to dictate the structure of the model. In this procedure, the non-local self-similarity property is utilized as a fruitful resource for abstracting a priori knowledge of images. On the other hand, the parametric part is used to explicitly model the inter-scale correlation, in which the local structure regularity is thoroughly explored to recover the sharp edges and major texture features of images. It is not respected if only the nonparametric modeling is considering. We achieve the best of both worlds within a multi-scale framework. Experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance with the state-of-the-art error concealment algorithms. ? 2012 IEEE.; EI; 0
语种	英语
DOI标识	10.1109/DCC.2012.25
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/412819]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Liu, Xianming,Zhai, Deming,Zhai, Guangtao,et al. Multi-scale spatial error concealment via hybrid Bayesian regression. 2012-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。