See-Through Image Blurring of Transparent Organic Light-Emitting Diodes Display: Calculation Method Based on Diffraction and Analysis of Pixel Structures

Qin, Zong; Tsai, Yu‐Hsiang; Yeh, Yen-Wei; Huang, Yi‐Pai; Shieh, Han–Ping D.

doi:10.1109/jdt.2016.2594815

Cited by 21 publications

(13 citation statements)

References 23 publications

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“…Note that the captured image was blurred by diffraction because the camera is behind the display 25 . Images of a resolution chart captured directly and captured through the display are compared in Figure 11, and Figure 12 is an image of a person captured through the display.…”

Section: Methodsmentioning

confidence: 99%

Evaluation and formulation of the sense of social telepresence in video‐mediated communication systems: Contribution of eye contact to enhancing social telepresence

2020

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Video‐mediated communication systems are generally used to enable face‐to‐face‐like communication between people in distant locations. However, in terms of social telepresence, video communication systems are generally inferior to actual face‐to‐face communication, which is a long‐standing challenge for online communication systems. Multiple studies have reported that several specific components, such as the resolution of the transmitted video and the width of the visual field, affect social telepresence separately. However, methods have not been established to comprehensively evaluate the sense of social telepresence provided by video communication systems, that is, integrated multiple component systems with various specifications. In this study, employing a regression analysis based on subjective evaluation results, we establish a new method to predict the sense of social telepresence based on the specifications of some system components. Using this formulation, it is clear that realizing eye contact and life‐size scaling make relatively large contributions to enhancing social telepresence. Finally, we develop a prototype system realizing eye contact and life‐size scaling and perform additional subjective evaluations to reconfirm the effectiveness of these components to enhance the sense of social telepresence in real‐time communication.

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Section: Methodsmentioning

confidence: 99%

Evaluation and formulation of the sense of social telepresence in video‐mediated communication systems: Contribution of eye contact to enhancing social telepresence

2020

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“…The simulator calculating see-through images of a transparent display with a given pixel structure was proposed in [7]. Figure 3 shows the architecture for the calculation.…”

Section: Review Of the Image Simulatormentioning

confidence: 99%

“…A pixel also includes an opaque area for emitters and other non-transparent components (e.g., TFT). Nevertheless, few commercialized smartphones are currently applied with under-display cameras (UDCs) because the image formation is severely distorted by the transparent display above [1][2][3][4][5][6][7][8]. The distortion is mainly caused by three factors.…”

Section: Introductionmentioning

confidence: 99%

“…For eliminating the diffraction-induced distortion, there are two lines of research. The first line seeks to modify the pixel structure for lighter diffraction, e.g., changing rectangular transparent windows to circle windows or interlacing transparent windows [1][2][3]7,8]. The other line adopts deep learning for image retrieval [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Even though simulated images may slightly differentiate from real images, the simulatorbased method can still select a small number of candidate pixel structures, which then spend acceptable cost and time for panel fabrication and image acquisition. Thus, this study will adopt a UDC image simulator proposed in our work [7] and seek to conduct an efficient panel design and image retrieval method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

P‐78: Simulator‐Based Efficient Panel Design and Image Retrieval for Under‐Display Cameras

Qin

Qiu

et al. 2021

Symp Digest of Tech Papers

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Images of an under‐display camera (UDC) are distorted by a transparent display's diffraction. Conventional panel design and image retrieval require high‐cost panel fabrication and time‐consuming image acquisition for deep learning. We utilize a UDC image simulator to perform pixel structure optimization and image retrieval efficiently. Four typical pixel structures are investigated and compared.

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43‐3: Pixel Structure Evaluation Regarding See‐through Image Quality for Transparent Displays: A Study Based on Diffraction Calculation and Full‐Reference Image Quality Assessment

Qin¹,

Huang

Shieh³

2017

Symp Digest of Tech Papers

Self Cite

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Through a series of diffracted see-through images of transparent displays, "multiscale structural similarity" is demonstrated to be the most adapted for quality assessment among several mainstream algorithms. Then aperture ratios, resolutions, and pixel geometries are evaluated and revealed to affect see-through image quality from the most significantly to the least. Author KeywordsTransparent display; see-through image; diffraction; image quality assessment; pixel structure 1. 43-3 / Z. Qin SID 2017 DIGEST • 615ISSN 0097-996X/17/4701-0615-$1.00

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See-Through Image Blurring of Transparent Organic Light-Emitting Diodes Display: Calculation Method Based on Diffraction and Analysis of Pixel Structures

Cited by 21 publications

References 23 publications

Evaluation and formulation of the sense of social telepresence in video‐mediated communication systems: Contribution of eye contact to enhancing social telepresence

Evaluation and formulation of the sense of social telepresence in video‐mediated communication systems: Contribution of eye contact to enhancing social telepresence

P‐78: Simulator‐Based Efficient Panel Design and Image Retrieval for Under‐Display Cameras

43‐3: Pixel Structure Evaluation Regarding See‐through Image Quality for Transparent Displays: A Study Based on Diffraction Calculation and Full‐Reference Image Quality Assessment

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