High Dynamic Range Image Compression by Optimizing Tone Mapped Image Quality Index

Tone mapping operators (TMOs) aim to compress high dynamic range (HDR) images to low dynamic range (LDR) ones so as to visualize HDR images on standard displays. Most existing TMOs were demonstrated on specific examples without being thoroughly evaluated using well-designed and subject-validated ima...

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Veröffentlicht in:IEEE transactions on image processing Jg. 24; H. 10; S. 3086 - 3097
Hauptverfasser: Kede Ma, Yeganeh, Hojatollah, Kai Zeng, Zhou Wang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.10.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Dynamic range
High dynamic range image
Image coding
Image contrast
Image enhancement
Image quality
image quality assessment
Indexes
Mapping
Mathematical models
Optimization
perceptual image processing
Searching
statistical naturalness
structural similarity
tone mapping operator
Upper bound
structural similarity
perceptual image processing
image quality assessment
statistical naturalness
High dynamic range image
tone mapping operator
ISSN:1057-7149, 1941-0042, 1941-0042
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Zusammenfassung:Tone mapping operators (TMOs) aim to compress high dynamic range (HDR) images to low dynamic range (LDR) ones so as to visualize HDR images on standard displays. Most existing TMOs were demonstrated on specific examples without being thoroughly evaluated using well-designed and subject-validated image quality assessment models. A recently proposed tone mapped image quality index (TMQI) made one of the first attempts on objective quality assessment of tone mapped images. Here, we propose a substantially different approach to design TMO. Instead of using any predefined systematic computational structure for tone mapping (such as analytic image transformations and/or explicit contrast/edge enhancement), we directly navigate in the space of all images, searching for the image that optimizes an improved TMQI. In particular, we first improve the two building blocks in TMQI-structural fidelity and statistical naturalness components-leading to a TMQI-II metric. We then propose an iterative algorithm that alternatively improves the structural fidelity and statistical naturalness of the resulting image. Numerical and subjective experiments demonstrate that the proposed algorithm consistently produces better quality tone mapped images even when the initial images of the iteration are created by the most competitive TMOs. Meanwhile, these results also validate the superiority of TMQI-II over TMQI.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2015.2436340