Quality Assessment of User-Generated Video Using Camera Motion

Guo, Jinlin; Gurrin, Cathal; Hopfgartner, Frank; Zhang, Zhenxing; Lao, Songyang

doi:10.1007/978-3-642-35725-1_44

Cited by 2 publications

(3 citation statements)

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“…The visual quality of video is highly relevant to three properties of camera motion (CM) [6], that is, speed, direction and acceleration. These properties affect video quality in different ways.…”

Section: Camera Motion Featurementioning

confidence: 99%

“…Specifically, we adopt the block-match based optical flow approach in [6] to detect the background or camera motion features, since it is computational efficient. For a video clip c, by the method in [6], a set of motion vectors is obtained, set as V = {v k−1,k } n , where, v k−1,k is the CM vector extracted from the consecutive frame k − 1 and k, n is the number of motion vectors extracted in c. Based on V , a set of acceleration vectors can be calculated, set as…”

Section: Camera Motion Featurementioning

confidence: 99%

“…For a video clip c, by the method in [6], a set of motion vectors is obtained, set as V = {v k−1,k } n , where, v k−1,k is the CM vector extracted from the consecutive frame k − 1 and k, n is the number of motion vectors extracted in c. Based on V , a set of acceleration vectors can be calculated, set as…”

Section: Camera Motion Featurementioning

confidence: 99%

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Who produced this video, amateur or professional?

Guo

Gurrin

Lao³

2013

Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval

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As the increasing affordability for capturing and storing video and the proliferation of Web 2.0 applications, video content is no longer necessarily created and supplied by a limited number of professional producers; any amateur can produce and publish his/her video quickly. Therefore, the amount of both professional-produced as well as amateur-produced video on the web is ever increasing. In this work, we propose a question; whether we can automatically classify an Internet video clip as being either professional-produced or amateur-produced? Hence, we investigate features and classification methods to answer this question. Based on the differences in the production processes of these two video categories, four features including camera motion, structure, audio feature and combined feature are adopted and studied along with with four popular classifiers KNN, SVM GMM and C4.5. Extensive experiments over carefully-constructed, representative datasets, evaluate these features and classifiers under different settings and compare to existing techniques. Experimental results demonstrate that SVMs with multimodal features from multi-sources are more effective at classifying video type. Finally, for answering the proposed question, results also show that automatically classifying a clip as professional-produced video or amateur-produced video can be achieved with good accuracy.

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Section: Camera Motion Featurementioning

confidence: 99%

Section: Camera Motion Featurementioning

confidence: 99%