QQ客服 官方微博 反馈留言

	Sparse Structural Similarity for Objective Image Quality Assessment
	Zhang, Xiang ; Wang, Shiqi ; Gu, Ke ; Jiang, Tingting ; Ma, Siwei ; Gao, Wen
	2015
关键词	Image quality assessment (IQA) orthogonal matching pursuit (OMP) sparse representation NATURAL IMAGES INDEX
英文摘要	In this paper, a novel full-reference (FR) image quality assessment (IQA) metric based on sparse representation is proposed. Sparse representation has been widely applied in many applications such as image denoising and restoration. It is a high-efficiency way in representing sparse and redundant natural images. Also it has been shown to be highly related to the human visual perception, which is characterized by a set of responses of neurons in visual cortex. In this paper, the sparse representation is applied in decomposing natural images into multiple layers depending on the visual importance. Inspired by these observations, a novel IQA metric called sparse structural similarity is proposed by measuring the fidelity of the stimulation of visual cortices. Experimental results on public databases indicate that the proposed method is effective in predicting subjective evaluation and as compared to state-of-the-art FR-IQA methods.; EI; CPCI-S(ISTP); 1561-1566
语种	英语
出处	2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS
DOI标识	10.1109/SMC.2015.276
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436338]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Xiang,Wang, Shiqi,Gu, Ke,et al. Sparse Structural Similarity for Objective Image Quality Assessment. 2015-01-01.

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。