The Experts below are selected from a list of 14154 Experts worldwide ranked by ideXlab platform
Gowri Shankar D Reddy - One of the best experts on this subject based on the ideXlab platform.
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efficient tone mapped Image quality index for high dynamic range Image compression
International Journal of Research, 2018Co-Authors: Arusuru Vinodkumar, Gowri Shankar D ReddyAbstract: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 1 .
Zhou Wang - One of the best experts on this subject based on the ideXlab platform.
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high dynamic range Image compression by optimizing tone mapped Image quality index
IEEE Transactions on Image Processing, 2015Co-Authors: Hojatollah Yeganeh, Kai Zeng, Zhou WangAbstract: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. 1 1 Partial preliminary results of this work were presented at ICASSP 2013 and ICME 2014.
Arusuru Vinodkumar - One of the best experts on this subject based on the ideXlab platform.
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efficient tone mapped Image quality index for high dynamic range Image compression
International Journal of Research, 2018Co-Authors: Arusuru Vinodkumar, Gowri Shankar D ReddyAbstract: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 1 .
Hojatollah Yeganeh - One of the best experts on this subject based on the ideXlab platform.
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high dynamic range Image compression by optimizing tone mapped Image quality index
IEEE Transactions on Image Processing, 2015Co-Authors: Hojatollah Yeganeh, Kai Zeng, Zhou WangAbstract: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. 1 1 Partial preliminary results of this work were presented at ICASSP 2013 and ICME 2014.
Kai Zeng - One of the best experts on this subject based on the ideXlab platform.
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high dynamic range Image compression by optimizing tone mapped Image quality index
IEEE Transactions on Image Processing, 2015Co-Authors: Hojatollah Yeganeh, Kai Zeng, Zhou WangAbstract: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. 1 1 Partial preliminary results of this work were presented at ICASSP 2013 and ICME 2014.