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Perceptually motivated loss functions for computer generated holographic displays.

ABSTRACT: Understanding and improving the perceived quality of reconstructed images is key to developing computer-generated holography algorithms for high-fidelity holographic displays. However, current algorithms are typically optimized using mean squared error, which is widely criticized for its poor correlation with perceptual quality. In our work, we present a comprehensive analysis of employing contemporary image quality metrics (IQM) as loss functions in the hologram optimization process. Extensive objective and subjective assessment of experimentally reconstructed images reveal the relative performance of IQM losses for hologram optimization. Our results reveal that the perceived image quality improves considerably when the appropriate IQM loss function is used, highlighting the value of developing perceptually-motivated loss functions for hologram optimization.

SUBMITTER: Yang F

PROVIDER: S-EPMC9095705 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Perceptually motivated loss functions for computer generated holographic displays.

Yang Fan F Kadis Andrew A Mouthaan Ralf R Wetherfield Benjamin B Kaczorowski Andrzej A Wilkinson Timothy D TD

Scientific reports 20220511 1

Understanding and improving the perceived quality of reconstructed images is key to developing computer-generated holography algorithms for high-fidelity holographic displays. However, current algorithms are typically optimized using mean squared error, which is widely criticized for its poor correlation with perceptual quality. In our work, we present a comprehensive analysis of employing contemporary image quality metrics (IQM) as loss functions in the hologram optimization process. Extensive ...[more]

PMID: 35546601

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