Improved entropy of primitive for visual information estimation

Shurun Wang; Zhenghui Zhao; Xiang Zhang; Jian Zhang; Shiqi Wang; Siwei Ma; Wen Gao

doi:10.1109/VCIP.2016.7805589

Improved entropy of primitive for visual information estimation

Shurun Wang
, Zhenghui Zhao
, Xiang Zhang
, Jian Zhang
, Shiqi Wang
, Siwei Ma
, Wen Gao

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

Sparse representation has been observed to be highly efficient in dealing with rich, varied and directional information in natural scenes. Based on the statistical analysis of primitives in sparse coding, the entropy of primitive (EoP) was proposed for measuring visual information of images, and its changing tendency has been shown to be highly relevant with the human visual system (HVS). But the sparse coefficient energy was ignored when calculating EoP, which may be critical in accounting for the primitive characteristics. To tackle this, an improved EoP is developed in this work via ℓ₂ norm calculation. We further give mathematical derivations for its convergence verification. Experimental evaluations have also demonstrated that the improved EoP can achieve more stable convergence tendencies, which is consistent with the perceptual experiences.

Original language	English
Title of host publication	VCIP 2016 : the 30th Anniversary of Visual Communication and Image Processing
Publisher	IEEE
ISBN (Electronic)	9781509053162
ISBN (Print)	978-1-5090-5317-9
DOIs	https://doi.org/10.1109/VCIP.2016.7805589
Publication status	Published - Dec 2016
Externally published	Yes
Event	VCIP 2016 : International Conference on Visual Communications and Image Processing - Chengdu, China Duration: 27 Nov 2016 → 30 Nov 2016 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=51810&copyownerid=85341

Publication series

Name	Visual Communications and Image Processing

Conference

Conference	VCIP 2016 : International Conference on Visual Communications and Image Processing
Abbreviated title	VCIP 2016
Place	China
City	Chengdu
Period	27/11/16 → 30/11/16
Internet address	http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=51810&copyownerid=85341

Research Keywords

Entropy of primitive
orthogonal matching pursuit
sparse representation
visual information estimation

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10.1109/VCIP.2016.7805589

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@inproceedings{d120f63b38984341b99702af7e3c4b36,

title = "Improved entropy of primitive for visual information estimation",

abstract = "Sparse representation has been observed to be highly efficient in dealing with rich, varied and directional information in natural scenes. Based on the statistical analysis of primitives in sparse coding, the entropy of primitive (EoP) was proposed for measuring visual information of images, and its changing tendency has been shown to be highly relevant with the human visual system (HVS). But the sparse coefficient energy was ignored when calculating EoP, which may be critical in accounting for the primitive characteristics. To tackle this, an improved EoP is developed in this work via ℓ2 norm calculation. We further give mathematical derivations for its convergence verification. Experimental evaluations have also demonstrated that the improved EoP can achieve more stable convergence tendencies, which is consistent with the perceptual experiences.",

keywords = "Entropy of primitive, orthogonal matching pursuit, sparse representation, visual information estimation",

author = "Shurun Wang and Zhenghui Zhao and Xiang Zhang and Jian Zhang and Shiqi Wang and Siwei Ma and Wen Gao",

year = "2016",

month = dec,

doi = "10.1109/VCIP.2016.7805589",

language = "English",

isbn = "978-1-5090-5317-9",

series = "Visual Communications and Image Processing",

publisher = "IEEE",

booktitle = "VCIP 2016 : the 30th Anniversary of Visual Communication and Image Processing",

address = "United States",

note = "VCIP 2016 : International Conference on Visual Communications and Image Processing, VCIP 2016 ; Conference date: 27-11-2016 Through 30-11-2016",

url = "http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=51810\&copyownerid=85341",

}

Wang, S, Zhao, Z, Zhang, X, Zhang, J, Wang, S, Ma, S & Gao, W 2016, Improved entropy of primitive for visual information estimation. in VCIP 2016 : the 30th Anniversary of Visual Communication and Image Processing., 7805589, Visual Communications and Image Processing, IEEE, VCIP 2016 : International Conference on Visual Communications and Image Processing, Chengdu, China, 27/11/16. https://doi.org/10.1109/VCIP.2016.7805589

Improved entropy of primitive for visual information estimation. / Wang, Shurun; Zhao, Zhenghui; Zhang, Xiang et al.
VCIP 2016 : the 30th Anniversary of Visual Communication and Image Processing. IEEE, 2016. 7805589 (Visual Communications and Image Processing).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Improved entropy of primitive for visual information estimation

AU - Wang, Shurun

AU - Zhao, Zhenghui

AU - Zhang, Xiang

AU - Zhang, Jian

AU - Wang, Shiqi

AU - Ma, Siwei

AU - Gao, Wen

PY - 2016/12

Y1 - 2016/12

N2 - Sparse representation has been observed to be highly efficient in dealing with rich, varied and directional information in natural scenes. Based on the statistical analysis of primitives in sparse coding, the entropy of primitive (EoP) was proposed for measuring visual information of images, and its changing tendency has been shown to be highly relevant with the human visual system (HVS). But the sparse coefficient energy was ignored when calculating EoP, which may be critical in accounting for the primitive characteristics. To tackle this, an improved EoP is developed in this work via ℓ2 norm calculation. We further give mathematical derivations for its convergence verification. Experimental evaluations have also demonstrated that the improved EoP can achieve more stable convergence tendencies, which is consistent with the perceptual experiences.

AB - Sparse representation has been observed to be highly efficient in dealing with rich, varied and directional information in natural scenes. Based on the statistical analysis of primitives in sparse coding, the entropy of primitive (EoP) was proposed for measuring visual information of images, and its changing tendency has been shown to be highly relevant with the human visual system (HVS). But the sparse coefficient energy was ignored when calculating EoP, which may be critical in accounting for the primitive characteristics. To tackle this, an improved EoP is developed in this work via ℓ2 norm calculation. We further give mathematical derivations for its convergence verification. Experimental evaluations have also demonstrated that the improved EoP can achieve more stable convergence tendencies, which is consistent with the perceptual experiences.

KW - Entropy of primitive

KW - orthogonal matching pursuit

KW - sparse representation

KW - visual information estimation

UR - https://www.scopus.com/pages/publications/85011016519

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85011016519&origin=recordpage

U2 - 10.1109/VCIP.2016.7805589

DO - 10.1109/VCIP.2016.7805589

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-5090-5317-9

T3 - Visual Communications and Image Processing

BT - VCIP 2016 : the 30th Anniversary of Visual Communication and Image Processing

PB - IEEE

T2 - VCIP 2016 : International Conference on Visual Communications and Image Processing

Y2 - 27 November 2016 through 30 November 2016

ER -

Improved entropy of primitive for visual information estimation

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