Digital Multimedia Perception And Design by Georghita Ghinea, Sherry Y. Chen

By Georghita Ghinea, Sherry Y. Chen

Electronic Multimedia notion and layout offers a well-rounded synopsis of the cutting-edge applied sciences in perceptual-based multimedia layout. it's the in simple terms such publication out there, uniting state-of-the-art examine via a few of the most sensible humans within the box and filling a niche within the current multimedia layout literature. electronic Multimedia conception and layout highlights how perceptual standards could be integrated within the layout, improvement and transmission of multimedia, offering readers with a wealthy physique of study and paintings performed during this intriguing quarter. This publication aids all these attracted to development real end-to-end multimedia details platforms, and turns out to be useful to researchers, multimedia designers, and content material and infrastructure services.

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Wh-semantics include: time (when), people (who), location (where), landmarks (what), and event (inferred from when, who, where, and what). Providing the when and where information is trivial. Already cameras can provide time, and we can easily infer location from GPS or CellID. However, determining the what and who requires contextual information in addition to time, location, and photo content. More precisely, contextual information provided by cameras includes time, location, camera parameters, user profile, and voice.

90-98). San Jose, CA: SPIE. , & Jones, M. (2004). Robust real-time object detection. International Journal of Computer Vision, 57(2), 137-154. VQEG. (2000). Final report from the Video Quality Experts Group on the validation of objective models of video quality assessment. org/ Wandell, B. (1995). Foundations of vision. Sunderland, MA: Sinauer Associates. Winkler, S. (1999). A perceptual distortion metric for digital color video. Proceedings of SPIE Human Vision and Electronic Imaging: Vol. 3644 (pp.

32 Chang Many methods have been proposed for performing dimensionality reduction, such as principal component analysis (PCA) and independent component analysis (ICA). Recently, several dimensionality-reduction algorithms have been proposed to find nonlinear manifolds embedded in a high-dimensional space. Among the proposed methods, Isomap (Tenenbaum, Silva, & Langford, 2000), local linear embedding (LLE) (Bengio, Paiement, Vincent, Delalleau, Roux, & Quimet, 2004), and kernel PCA (KPCA) (Ham, Lee, Mika, & Scholkopf, 2004) have been applied to tasks of image clustering and image retrieval (He, Ma, & Zhang, 2004).

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