Information geometry of U-Boost and Bregman divergence
Neural Computation
An information theoretic analysis of maximum likelihood mixture estimation for exponential families
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Error concealment by means of clustered blockwise PCA
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
IEEE Transactions on Image Processing
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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.