NDLI: Structured entropy of primitive: big data-based stereoscopic image quality assessment

Content Provider	IET Digital Library
Author	Liu, Zhiguo Yang, Chifu Rho, Seungmin Liu, Shaohui Jiang, Feng
Abstract	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.
Starting Page	854
Ending Page	860
Page Count	7
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 10, Oct (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.1053
Journal	IET Image Processing
Publisher Date	2017-04-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Big Data Computer Vision And Image Processing Technique Data Handling Technique Human Visual System HVS HVS Perception Property Image Processing Image Receiver Image Structured Information Knowledge Engineering Technique Optical, Image And Video Signal Processing Regression Analysis Statistics Stereo Image Processing Stereoscopic Image Information Stereoscopic Image Quality Assessment Metric Structured Entropy Support Vector Machine Support Vector Regression Video Processing Video Receiver Video Signal Processing Visual Perception
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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