Abstract: A method and system for of single view image 3D face synthesis. The method comprises the steps of a) extracting feature points from the single view image; b) transforming the feature points into 3D space; c) calculating radial basis function (RBF) parameters in 3D space based on the transformed feature points and corresponding points from a 3D generic model; d) applying RBF deformation to the generic 3D model based on the RBF parameters to determine a model for the synthesized 3D face; and e) determining texture coordinates for the synthesized 3D face in 2D image space; wherein step b) comprises symmetrically aligning the feature points, and step e) comprises projecting the generic 3D model or the model for the synthesized 3D face into 2D image space and applying RBF deformation to the projected generic 3D model or the projected model for the synthesized 3D face.

Abstract: A new approach to objective quality assessment of DCT-coded video sequences, with or without a reference is proposed. The system is comprised of a proprietary segmentation algorithm, a feature extraction process and a nonlinear feed-forward-type neural network for feature analysis. The methods mimic function of the human visual system (HVS): A neural network training algorithm is used for determining the optimal network weights and biases for both system modes of operation. The proposed method allows for assessment of DCT-coded video sequences without the original source being available (pseudo-reference mode). The pseudo-reference mode is also comprised of a proprietary DCT-coded video (MPEG) noise reducer (MNR), co-pending patent application No. 60/592,143.