文献类型：期刊文献

英文题名：Structured entropy of primitive: big data-based stereoscopic image quality assessment

作者：Liu, Zhiguo[1];Yang, Chifu[2];Rho, Seungmin[3];Liu, Shaohui[2];Jiang, Feng[2]

第一作者：刘治国

通讯作者：Liu, ZG[1]

机构：[1]Beijing Union Univ, Coll Informat Technol, Beijing 100101, Peoples R China;[2]Harbin Inst Technol, Harbin 150001, Heilongjiang, Peoples R China;[3]Sungkyul Univ, Dept Media Software, Anyang, South Korea

第一机构：北京联合大学智慧城市学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Informat Technol, Beijing 100101, Peoples R China.|[1141734]北京联合大学智慧城市学院;[11417]北京联合大学;

年份：2017

卷号：11

期号：10

起止页码：854-+

外文期刊名：IET IMAGE PROCESSING

收录：;EI(收录号：20174304289648);Scopus(收录号：2-s2.0-85031670608);WOS:【SCI-EXPANDED(收录号:WOS:000413198200007)】；

基金：This work was partially funded by the MOE-Microsoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, the Major State Basic Research Development Program of China (973 Program 2015CB351804) and the National Natural Science Foundation of China under grant no. 61572155, 61672188 and 61272386.

语种：英文

外文关键词：Big data - Binoculars - Entropy - Stereo image processing - Support vector regression - Video signal processing

摘要：The ultimate receiver of image and video is human visual system (HVS). It is an important problem in the domain of image and video processing that how to establish visual information representation model meeting the HVS perception property. In this study, authors give theory analysis and experiment results to prove that l_1 norm-based entropy of primitive (EoP) is superior to the l_0 norm-based EoP for the monocular cue in image quality assessment. By developing the concept of mutual information of primitive (MIP) as the binocular cue, an l_1 EoP-based stereoscopic image quality assessment metric is proposed. With EoP as monocular cue and MIP as binocular cue, the relative entropy between the original stereoscopic image and the distorted one is explored to predict the quality score with support vector regression. To avoid destroying image's structured information, the structured EoP (SEoP) is further explored to measure the stereoscopic image information. Extensive experimental results demonstrate that the stereoscopic image quality assessment algorithm with SEoP as monocular cue and MIP as binocular cue outperforms many state-of-the-art ones.