Browsing by Author "Lei, Chin-Laung"

Now showing 1 - 4 of 4

Data Hiding on Social Media Communications Using Text Steganography
(2018-01-01)
Shiu, Hung-Jr
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Lin, Bor-Shing
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Lin, Bor-Shyh
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Huang, Po-Yang
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Huang, Chien-Hung
This research work proposes a steganography on social media communications. Nowadays, people frequently use messenger or social network such as Skype, Line, Facebook, Whatsapp and Twitter, etc. to communicate with other people and these platforms become popular to be used to exchange secrets or preserve personal information. Personal information like accounts and passwords might not be seen and embedded to cover objects when two clients communicate to each other. Those objects shall be pictures, music, or text messages. The proposed scheme will be deployed on the cover text. A strategy is designed to increase the capacity of hidden data and try to make any simple piece of text to be the cover text; such as letters, article of news or the common messages. The new approach - Extended Line will be adopted, and then together with White Space between to increase the capacity of a text. The simulation results disclose that the algorithm not only increases the capacity, but also increases the efficiency of decoding. Moreover, it still works on any kind of cover text.
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
;
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.