Fractional-pel Accurate Motion-Adaptive Transforms.

Fractional-pel accurate motion is widely used in video coding. For subband coding, fractional-pel accuracy is challenging since it is difficult to handle the complex motion field with temporal transforms. In our previous work, we designed integer accurate motion-adaptive transforms (MAT) which can transform integer accurate motion-connected coefficients. In this paper, we extend the integer MAT to fractional-pel accuracy. The integer MAT allows only one reference coefficient to be the lowband coefficient. In this paper, we design the transform such that it permits multiple references and generates multiple lowband coefficients. In addition, our fractional-pel MAT can incorporate a general interpolation filter into the basis vector, such that the highband coefficient produced by the transform is the same as the prediction error from the interpolation filter. The fractional-pel MAT is always orthonormal. Thus, the energy is preserved by the transform. We compare the proposed fractionalpel MAT, the integer MAT, and the half-pel motion-compensated orthogonal transform (MCOT), while HEVC intra coding is used to encode the temporal subbands. The experimental results show that the proposed fractional-pel MAT outperforms the integer MAT and the half-pel MCOT. The gain achieved by the proposed MAT over the integer MAT can reach up to 1dB in PSNR.

Graph-preserving sparse nonnegative matrix factorization with application to facial expression recognition.

In this paper, a novel graph-preserving sparse nonnegative matrix factorization (GSNMF) algorithm is proposed for facial expression recognition. The GSNMF algorithm is derived from the original NMF algorithm by exploiting both sparse and graph-preserving properties. The latter may contain the class information of the samples. Therefore, GSNMF can be conducted as an unsupervised or a supervised dimension reduction method. A sparse representation of the facial images is obtained by minimizing the l(1)-norm of the basis images. Furthermore, according to the graph embedding theory, the neighborhood of the samples is preserved by retaining the graph structure in the mapped space. The GSNMF decomposition transforms the high-dimensional facial expression images into a locality-preserving subspace with sparse representation. To guarantee convergence, we use the projected gradient method to calculate the nonnegative solution of GSNMF. Experiments are conducted on the JAFFE database and the Cohn-Kanade database with unoccluded and partially occluded facial images. The results show that the GSNMF algorithm provides better facial representations and achieves higher recognition rates than nonnegative matrix factorization. Moreover, GSNMF is also more robust to partial occlusions than other tested methods.