KEYWORDS: Video, 3D modeling, Eye models, Telecommunications, Video compression, Image compression, RGB color model, Systems modeling, Eye, Data modeling
Emerging communications trends point to streaming video as a new form of content delivery. These systems are
implemented over wired systems, such as cable or ethernet, and wireless networks, cell phones, and portable game
systems. These communications systems require sophisticated methods of compression and error-resilience encoding to
enable communications across band-limited and noisy delivery channels. Additionally, the transmitted video data must
be of high enough quality to ensure a satisfactory end-user experience. Traditionally, video compression makes use of
temporal and spatial coherence to reduce the information required to represent an image. In many communications
systems, the communications channel is characterized by a probabilistic model which describes the capacity or fidelity
of the channel. The implication is that information is lost or distorted in the channel, and requires concealment on the
receiving end. We demonstrate a generative model based transmission scheme to compress human face images in video,
which has the advantages of a potentially higher compression ratio, while maintaining robustness to errors and data
corruption. This is accomplished by training an offline face model and using the model to reconstruct face images on the
receiving end. We propose a sub-component AAM modeling the appearance of sub-facial components individually, and
show face reconstruction results under different types of video degradation using a weighted and non-weighted version
of the sub-component AAM.
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