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Analysis of Eye Response to Video Quality and Structure
Title
Analysis of Eye Response to Video Quality and Structure.
Creator
Pappusetty, Deepti, Kalva, Hari, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Real-time eye tracking systems with human-computer interaction mechanism are being adopted to advance user experience in smart devices and consumer electronic systems. Eye tracking systems measure eye gaze and pupil response non-intrusively. This research presents an analysis of eye pupil and gaze response to video structure and content. The set of experiments for this study involved presenting different video content to subjects and measuring eye response with an eye tracker. Results show...
Show moreReal-time eye tracking systems with human-computer interaction mechanism are being adopted to advance user experience in smart devices and consumer electronic systems. Eye tracking systems measure eye gaze and pupil response non-intrusively. This research presents an analysis of eye pupil and gaze response to video structure and content. The set of experiments for this study involved presenting different video content to subjects and measuring eye response with an eye tracker. Results show significant changes in video and scene cuts led to sharp constrictions. User response to videos can provide insights that can improve subjective quality assessment metrics. This research also presents an analysis of the pupil and gaze response to quality changes in videos. The results show pupil constrictions for noticeable changes in perceived quality and higher fixations/saccades ratios with lower quality. Using real-time eye tracking systems for video analysis and quality evaluation can open a new class of applications for consumer electronic systems.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00005940
Subject Headings
Dissertations, Academic -- Florida Atlantic University, Eye tracking., Video., Quality (Aesthetics)
Format
Document (PDF)
IMAGE QUALITY AND BEAUTY CLASSIFICATION USING DEEP LEARNING
Title
IMAGE QUALITY AND BEAUTY CLASSIFICATION USING DEEP LEARNING.
Creator
Golchubian, Arash, Nojoumian, Mehrdad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
The field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding...
Show moreThe field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding artifacts, saturation, and lighting, as well as for its’ aesthetic appeal. The purpose of such a mechanism could be detecting and discarding noisy, blurry, dark, or over exposed images, as well as detecting images that would be considered beautiful by a majority of viewers. In this dissertation, the detection of various quality and aesthetic aspects of an image using CNNs is explored. This research produced two datasets that are manually labeled for quality issues such as blur, poor lighting, and digital noise, and for their aesthetic qualities, and Convolutional Neural Networks were designed and trained using these datasets. Lastly, two case studies were performed to show the real-world impact of this research to traffic sign detection and medical image diagnosis.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00014029
Subject Headings
Deep learning (Machine learning), Computer vision, Aesthetics, Image Quality
Format
Document (PDF)