Browsing by Author "Hsu, Chih-Fan"

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Is Foveated Rendering Perceivable in Virtual Reality? Exploring the Efficiency and Consistency of Quality Assessment Methods
(2017-01-01)
Hsu, Chih-Fan
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Chen, Anthony
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Hsu, Cheng-Hsin
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Huang, Chun-Ying
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Lei, Chin-Laung
Foveated rendering leverages human visual system to increase video quality under limited computing resources for Virtual Reality (VR). More specifically, it increases the frame rate and the video quality of the foveal vision via lowering the resolution of the peripheral vision. Optimizing foveated rendering systems is, however, not an easy task, because there are numerous parameters that need to be carefully chosen, such as the number of layers, the eccentricity degrees, and the resolution of the peripheral region. Furthermore, there is no standard and efficient way to evaluate the Quality of Experiment (QoE) of foveated rendering systems. In this paper, we propose a framework to compare the performance of different subjective assessment methods on foveated rendering systems. We consider two performance metrics: efficiency and consistency, using the perceptual ratio, which is the probability of the foveated rendering is perceivable by users. A regression model is proposed to model the relationship between the human perceived quality and foveated rendering parameters. Our comprehensive study and analysis reveal several insights: 1) there is no absolute superior subjective assessment method, 2) subjects need to make more observations to confirm the foveated rendering is imperceptible than perceptible, 3) subjects barely notice the foveated rendering with an eccentricity degree of 7.5 degrees + and peripheral region of a resolution of 540p+, and 4) QoE levels are highly dependent on the individuals and scenes. Our findings are crucial for optimizing the foveated rendering systems for future VR applications.
Look at Me! Correcting Eye Gaze in Live Video Communication
(2019-06-01)
Hsu, Chih-Fan
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Wang, Yu-Shuen
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Lei, Chin-Laung
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Chen, Kuan-Ta
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資訊工程學系
Although live video communication is widely used, it is generally less engaging than face-to-face communication because of limitations on social, emotional, and haptic feedback. Missing eye contact is one such problem caused by the physical deviation between the screen and camera on a device. Manipulating video frames to correct eye gaze is a solution to this problem. In this article, we introduce a system to rotate the eyeball of a local participant before the video frame is sent to the remote side. It adopts a warping-based convolutional neural network to relocate pixels in eye regions. To improve visual quality, we minimize the L2 distance between the ground truths and warped eyes. We also present several newly designed loss functions to help network training. These new loss functions are designed to preserve the shape of eye structures and minimize color changes around the periphery of eye regions. To evaluate the presented network and loss functions, we objectively and subjectively compared results generated by our system and the state-of-the-art, DeepWarp, in relation to two datasets. The experimental results demonstrated the effectiveness of our system. In addition, we showed that our system can perform eye-gaze correction in real time on a consumer-level laptop. Because of the quality and efficiency of the system, gaze correction by postprocessing through this system is a feasible solution to the problem of missing eye contact in video communication.
Realizing the Real-time Gaze Redirection System with Convolutional Neural Network
(2018-01-01)
Hsu, Chih-Fan
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Chen, Yu-Cheng
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Wang, Yu-Shuen
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Lei, Chin-Laung
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Chen, Kuan-Ta
Retaining eye contact of remote users is a critical issue in video conferencing systems because of parallax caused by the physical distance between a screen and a camera. To achieve this objective, we present a real-time gaze redirection system called Flx-gaze to post-process each video frame before sending it to the remote end. Specifically, we relocate and relight the pixels representing eyes by using a convolutional neural network (CNN). To prevent visual artifacts during manipulation, we minimize not only the L2 loss function but also four novel loss functions when training the network. Two of them retain the rigidity of eyeballs and eyelids; and the other two prevent color discontinuity on the eye peripheries. By leveraging the CPU and the GPU resources, our implementation achieves real-time performance (i.e., 31 frames per second). Experimental results show that the gazes redirected by our system are of high quality under this restrict time constraint. We also conducted an objective evaluation of our system by measuring the peak signal-to-noise ratio (PSNR) between the real and the synthesized images.
Smart Beholder: An Extensible Smart Lens Platform
(2016)
Huang, Chun-Ying
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Fan, Ching-Ling
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Hsu, Chih-Fan
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Chang, Hsin-Yu
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Tsai, Tsung-Han
Smart Lenses refer to detachable, orientable and zoomable lenses that stream live videos over wireless networks to heterogeneous computing devices, including tablets and smartphones. Various novel applications are made possible by smart lenses, including mobile photography, smart surveillance cameras, and Unmanned Aerial Vehicle (UAV) cameras. However, to our best knowledge, existing smart lenses are closed and proprietary, and thus we initiate an open-source project called Smart Beholder for end-to-end solutions of smart lenses. The code and documents of Smart Beholder can be found at our website http://www.smartbeholder.org. Our Smart Beholder platform are useful to researchers for fast prototyping, developers for rapid development, and amateurs for hobbies. We have implemented Smart Beholder server (camera) using a popular embedded Linux platform, called Raspberry Pi. We have also realized Smart Beholder client (controller) on various OS\'s, including Android. Our experimental results show the practicality and efficiency of our proposed Smart Beholder: we outperform commercial products in the market in terms of both objective and subjective metrics. We believe the release of Smart Beholder will stimulate future studies on novel multimedia applications enabled by smart lenses.
Toward an Adaptive Screencast Platform: Measurement and Optimization
(2016-12-01)
Hsu, Chih-Fan
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Fan, Ching-Ling
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Tsai, Tsung-Han
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Huang, Chun-Ying
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Hsu, Cheng-Hsin
The binding between computing devices and displays is becoming dynamic and adaptive, and screencast technologies enable such binding over wireless networks. In this article, we design and conduct the first detailed measurement study on the performance of the state-of-the-art screencast technologies. Several commercial and one open-source screencast technologies are considered in our detailed analysis, which leads to several insights: (1) there is no single winning screencast technology, indicating room to further enhance the screencast technologies; (2) hardware video encoders significantly reduce the CPU usage at the expense of slightly higher GPU usage and end-to-end delay, and should be adopted in future screencast technologies; (3) comprehensive error resilience tools are needed as wireless communication is vulnerable to packet loss; (4) emerging video codecs designed for screen contents lead to a better Quality of Experience (QoE) of screencast; and (5) rate adaptation mechanisms are critical to avoiding degraded QoE due to network dynamics. As a case study, we propose a nonintrusive yet accurate available bandwidth estimation mechanism. Real experiments demonstrate the practicality and efficiency of our proposed solution. Our measurement methodology, open-source screencast platform, and case study allow researchers and developers to quantitatively evaluate other design considerations, which will lead to optimized screencast technologies.