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dc.contributor.authorAitha, Naveen Kumar
dc.description.abstractThe use of multimedia-enabled mobile devices such as pocket PC's, smart cell phones and PDA's is increasing by the day and at a rapid pace. Networked environments comprising of these multimedia-enabled mobile devices are typically resource constrained in terms of their battery capacity and available bandwidth. Real-time computer vision applications typically entail the analysis, storage, transmission, and rendering of video data, and are hence resource-intensive. Consequently, it is very important to develop a content-aware video encoding scheme that adapts dynamically to and makes efficient use of the available resources. A Hybrid Multi-Layered Video (HMLV) encoding scheme is proposed which comprises of content-aware, multi-layer wavelet-based encoding of the image texture and motion, and a generative sketch-based representation of the object outlines. Each video layer in the proposed scheme is characterized by a distinct resource consumption profile. Experimental results on real video data show that the proposed scheme is effective for computer vision and multimedia applications such as face recognition and activity recognition in resource-constrained mobile network environments.
dc.subjectlayered media, Video streaming, Activity Recognition, Face Recognition
dc.titleA hybrid multi-layer wavelet-based video encoding scheme for computer vision applications on mobile resource constrained devices
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorSuchendra M. Bhandarkar
dc.description.committeeSuchendra M. Bhandarkar
dc.description.committeeTianming Liu
dc.description.committeeKang Li

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