Objective Quality Assessment of Tone-Mapped Images

Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best qu...

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Vydané v:	IEEE transactions on image processing Ročník 22; číslo 2; s. 657 - 667
Hlavní autori:	Yeganeh, H., Zhou Wang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.02.2013 Institute of Electrical and Electronics Engineers
Predmet:	Applied sciences Brightness Correlation Detection, estimation, filtering, equalization, prediction Dynamic range Exact sciences and technology High dynamic range image image fusion Image processing image quality assessment Information, signal and communications theory naturalness perceptual image processing Quality assessment Sensitivity Signal and communications theory Signal processing Signal, noise structural similarity Telecommunications and information theory tone mapping operator Visualization Dynamic response perceptual image processing Similarity Image processing Objective analysis Image databank structural similarity Timbre Algorithm High dynamic range image Subjective evaluation Optimization Image quality HDR image Tone mapping Multiscale method naturalness Quality control image quality assessment Image fusion Image evaluation tone mapping operator
ISSN:	1057-7149, 1941-0042, 1941-0042
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Abstract	Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of TMQI using two examples - parameter tuning for TMOs and adaptive fusion of multiple tone-mapped images.
AbstractList	Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of TMQI using two examples-parameter tuning for TMOs and adaptive fusion of multiple tone-mapped images.Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of TMQI using two examples-parameter tuning for TMOs and adaptive fusion of multiple tone-mapped images. Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Different TMOs create different tone-mapped images, and a natural question is which one has the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive and time consuming, and more importantly, is difficult to be embedded into optimization frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using independent subject-rated image databases show good correlations between subjective ranking score and the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of TMQI using two examples-parameter tuning for TMOs and adaptive fusion of multiple tone-mapped images.
Author	Zhou Wang Yeganeh, H.
Author_xml	– sequence: 1 givenname: H. surname: Yeganeh fullname: Yeganeh, H. email: hyeganeh@uwaterloo.ca organization: Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada – sequence: 2 surname: Zhou Wang fullname: Zhou Wang email: zhouwang@ieee.org organization: Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26853954$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/23047872$$D View this record in MEDLINE/PubMed
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Keywords	Dynamic response perceptual image processing Similarity Image processing Objective analysis Image databank structural similarity Timbre Algorithm High dynamic range image Subjective evaluation Optimization Image quality HDR image Tone mapping Multiscale method naturalness Quality control image quality assessment Image fusion Image evaluation tone mapping operator
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Snippet	Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of...
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SubjectTerms	Applied sciences Brightness Correlation Detection, estimation, filtering, equalization, prediction Dynamic range Exact sciences and technology High dynamic range image image fusion Image processing image quality assessment Information, signal and communications theory naturalness perceptual image processing Quality assessment Sensitivity Signal and communications theory Signal processing Signal, noise structural similarity Telecommunications and information theory tone mapping operator Visualization
Title	Objective Quality Assessment of Tone-Mapped Images
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