Quality-aware images

Zhou Wang; Guixing Wu; Hamid Rahim Sheikh; Eero P. Simoncelli; En Hui Yang; Alan Conrad Bovik

doi:10.1109/TIP.2005.864165

Quality-aware images

Zhou Wang, Guixing Wu, Hamid Rahim Sheikh, Eero P. Simoncelli, En Hui Yang, Alan Conrad Bovik

Neural Science

Research output: Contribution to journal › Article › peer-review

Abstract

We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system,A MATLAB implementation of the system is available online at http:// www.cns.nyu.edu/lcv/qaware. which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.

Original language	English (US)
Pages (from-to)	1680-1689
Number of pages	10
Journal	IEEE Transactions on Image Processing
Volume	15
Issue number	6
DOIs	https://doi.org/10.1109/TIP.2005.864165
State	Published - Jun 2006

Keywords

Generalized Gaussian density (GGD)
Image communication
Image quality assessment
Image watermarking
Information hiding
Natural image statistics
Quality-aware image
Reduced-reference image quality assessment

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

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10.1109/TIP.2005.864165

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@article{27baef8462934d8484feaca33510dbd5,

title = "Quality-aware images",

abstract = "We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system,A MATLAB implementation of the system is available online at http:// www.cns.nyu.edu/lcv/qaware. which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.",

keywords = "Generalized Gaussian density (GGD), Image communication, Image quality assessment, Image watermarking, Information hiding, Natural image statistics, Quality-aware image, Reduced-reference image quality assessment",

author = "Zhou Wang and Guixing Wu and Sheikh, {Hamid Rahim} and Simoncelli, {Eero P.} and Yang, {En Hui} and Bovik, {Alan Conrad}",

note = "Funding Information: Manuscript received August 30, 2004; revised May 31, 2005. The work of Z. Wang and E. P. Simoncelli was supported in part by the Howard Hughes Medical Institute. The work of G. Wu and E.-H. Yang was supported in part by the National Sciences and Engineering Research Council of Canada, in part by the Premier{\textquoteright}s Research Excellence Award, Canada Foundation for Innovation, in part by the Ontario Distinguished Research Award, and in part by the Canada Research Chairs Program. The work of H. R. Sheikh and A. C. Bovik was supported in part by the National Science Foundation. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Zhigang Fan.",

year = "2006",

month = jun,

doi = "10.1109/TIP.2005.864165",

language = "English (US)",

volume = "15",

pages = "1680--1689",

journal = "IEEE Transactions on Image Processing",

issn = "1057-7149",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Wang, Zhou

AU - Wu, Guixing

AU - Sheikh, Hamid Rahim

AU - Simoncelli, Eero P.

AU - Yang, En Hui

AU - Bovik, Alan Conrad

N1 - Funding Information: Manuscript received August 30, 2004; revised May 31, 2005. The work of Z. Wang and E. P. Simoncelli was supported in part by the Howard Hughes Medical Institute. The work of G. Wu and E.-H. Yang was supported in part by the National Sciences and Engineering Research Council of Canada, in part by the Premier’s Research Excellence Award, Canada Foundation for Innovation, in part by the Ontario Distinguished Research Award, and in part by the Canada Research Chairs Program. The work of H. R. Sheikh and A. C. Bovik was supported in part by the National Science Foundation. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Zhigang Fan.

PY - 2006/6

Y1 - 2006/6

N2 - We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system,A MATLAB implementation of the system is available online at http:// www.cns.nyu.edu/lcv/qaware. which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.

AB - We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system,A MATLAB implementation of the system is available online at http:// www.cns.nyu.edu/lcv/qaware. which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.

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Quality-aware images

Abstract

Keywords

ASJC Scopus subject areas

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