Optimizing vision and visuals

Kavaklı, Koray; Walton, David R.; Antipa, Nick; Mantiuk, Rafał; Lanman, Douglas; Akşit, Kaan

doi:10.1145/3532720.3535650

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…1 to provide an image quality comparison of HoloHDR against the conventional scheme. In our assessments, we use commonly accepted image quality metrics of Peak Signal-to-noise Ratio (PSNR), Structural Similarity (SSIM), and Perceptual Similarity Metric (LPIPS) [35] (Readily available at GitHub:odak [36,37] and GitHub:piq libraries [38]). Our assessments compare the above two schemes for increasing intensity levels.…”

Section: Discussionmentioning

confidence: 99%

HoloHDR: Multi-color Holograms improve Dynamic Range

Akşit¹,

Kavaklı²,

Shi³

et al. 2023

Preprint

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Holographic displays generate Three-Dimensional (3D) images by displaying single-color holograms time-sequentially, each lit by a single-color light source. However, representing each color individually limits peak intensity (brightness) and dynamic range in holographic displays. This paper introduces a new driving scheme, HoloHDR, for realizing higher dynamic range images in holographic displays. Unlike the conventional driving scheme, HoloHDR utilizes three light sources to illuminate each displayed hologram simultaneously at various brightness levels. In this way, HoloHDR reconstructs a multiplanar three-dimensional target scene using consecutive multi-color holograms and persistence of vision. We co-optimize multi-color holograms and required brightness levels from each light source using a gradient descent-based optimizer with a combination of application-specific loss terms. We experimentally demonstrate that HoloHDR can increase the brightness levels in holographic displays up to three times with support for a broader dynamic range, unlocking new potentials for perceptual realism in holographic displays.

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Section: Discussionmentioning

confidence: 99%

HoloHDR: Multi-color Holograms improve Dynamic Range

Akşit¹,

Kavaklı²,

Shi³

et al. 2023

Preprint

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“…Leveraging the dataset and the described multi-color optimizations, we develop AutoColor , a light source power estimation CNN using PyTorch 20 and Odak, 21 where we estimate various l from input images. AutoColor includes downsampling blocks followed by a final convolutional layer, as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

AutoColor: learned light power control for multi-color holograms

Zhan,

Kavaklı,

Ürey

et al. 2024

Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V

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Multi-color holograms rely on simultaneous illumination from multiple light sources. These multi-color holograms could utilize light sources better than conventional single-color holograms and can improve the dynamic range of holographic displays. In this letter, we introduce AutoColor , the first learned method for estimating the optimal light source powers required for illuminating multi-color holograms. For this purpose, we establish the first multi-color hologram dataset using synthetic images and their depth information. We generate these synthetic images using a trending pipeline combining generative, large language, and monocular depth estimation models. Finally, we train our learned model using our dataset and experimentally demonstrate that AutoColor significantly decreases the number of steps required to optimize multi-color holograms from > 1000 to 70 iteration steps without compromising image quality.

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“…We use a multi-layer perceptron network that act as general function approximator for fitting a robust representation of raw data. Implementation of this can be found in (See

in [ 33 , 34 ]). Once we produce 2-D array-based data for color primaries, we utilize it to investigate the color perception responses of the Human Visual System.…”

Section: Perceptually Guided Prescription Correctionmentioning

confidence: 99%

ChromaCorrect: prescription correction in virtual reality headsets through perceptual guidance

Güzel¹,

Beyazian²,

Chakravarthula³

et al. 2023

Biomed. Opt. Express

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A large portion of today’s world population suffers from vision impairments and wears prescription eyeglasses. However, prescription glasses cause additional bulk and discomfort when used with virtual reality (VR) headsets, negatively impacting the viewer’s visual experience. In this work, we remedy the usage of prescription eyeglasses with screens by shifting the optical complexity into the software. Our proposal is a prescription-aware rendering approach for providing sharper and more immersive imagery for screens, including VR headsets. To this end, we develop a differentiable display and visual perception model encapsulating the human visual system’s display-specific parameters, color, visual acuity, and user-specific refractive errors. Using this differentiable visual perception model, we optimize the rendered imagery in the display using gradient-descent solvers. This way, we provide prescription glasses-free sharper images for a person with vision impairments. We evaluate our approach and show significant quality and contrast improvements for users with vision impairments.

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Optimizing vision and visuals

Cited by 6 publications

References 2 publications

HoloHDR: Multi-color Holograms improve Dynamic Range

HoloHDR: Multi-color Holograms improve Dynamic Range

AutoColor: learned light power control for multi-color holograms

ChromaCorrect: prescription correction in virtual reality headsets through perceptual guidance

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