Felipe P. do Carmo

Alessandra M. Coelho, Vania V. Estrela, Felipe P. do Carmo et al.

This work addresses the problem of error concealment in video transmission systems over noisy channels employing Bregman divergences along with regularization. Error concealment intends to improve the effects of disturbances at the reception due to bit-errors or cell loss in packet networks. Bregman regularization gives accurate answers after just some iterations with fast convergence, better accuracy, and stability. This technique has an adaptive nature: the regularization functional is updated according to Bregman functions that change from iteration to iteration according to the nature of the neighborhood under study at iteration n. Numerical experiments show that high-quality regularization parameter estimates can be obtained. The convergence is sped up while turning the regularization parameter estimation less empiric, and more automatic.

Felipe P. do Carmo, Vania Vieira Estrela, Joaquim Teixeira de Assis

In this paper, two simple principal component regression methods for estimating the optical flow between frames of video sequences according to a pel-recursive manner are introduced. These are easy alternatives to dealing with mixtures of motion vectors in addition to the lack of prior information on spatial-temporal statistics (although they are supposed to be normal in a local sense). The 2D motion vector estimation approaches take into consideration simple image properties and are used to harmonize regularized least square estimates. Their main advantage is that no knowledge of the noise distribution is necessary, although there is an underlying assumption of localized smoothness. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.