Automatic partitioning of full-motion video
Multimedia Systems
The nature of statistical learning theory
The nature of statistical learning theory
Learning No-Reference Quality Metric by Examples
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Multi-modality web video categorization
Proceedings of the international workshop on Workshop on multimedia information retrieval
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Blurred image detection and classification
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Blocking artifact detection and reduction in compressed data
IEEE Transactions on Circuits and Systems for Video Technology
Home Video Visual Quality Assessment With Spatiotemporal Factors
IEEE Transactions on Circuits and Systems for Video Technology
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The advent of video-sharing sites such as YouTube has led to an unprecedented Internet delivery of community-contributed video content. However, most of these videos are not quality-controlled. This paper reports a first attempt towards assessing web videos in terms of visual quality with significant tests on 30k web videos. We regard the quality assessment as a two-class classification problem: features motivated from domain knowledge are extracted to be the visual representation while the overall quality is the two-class label. Observing that web videos are characterized by a much higher diversity of content, genres, capture devices, and skills than any other traditional video program, we propose to combine two types of domain knowledge to predict the perceived quality score. One of the domain knowledge types is the spatiotemporal factors affecting the overall perceived quality of web videos, including four spatial factors and two temporal factors. We study the effectiveness of various spatiotemporal factors and propose some novel spatial factors pertaining the characteristics of web videos. The other is the video editing style, including shot editing style, frame size, and black side ratio. Comprehensive experiments and evaluations over 30k web videos which add up to 1200h in total demonstrated the effectiveness of the proposed approach. We show some preliminary results for application to filtering and re-ranking of retrieved web videos.