title: Image prediction based on neighbor-embedding methods.
abstract: This paper describes two new intraimage prediction methods based on two data dimensionality reduction methods: nonnegative matrix factorization (NMF) and locally linear embedding. These two methods aim at approximating a block to be predicted in the image as a linear combination of   k-nearest neighbors determined on the known pixels in a causal neighborhood of the input block. Variable  k can be seen as a parameter controlling some sort of sparsity constraints of the approximation vector. The impact of this parameter as well as of the nonnegativity and sum-to-one constraints for the addressed prediction problem has been analyzed. The prediction and RD performances of these two new image prediction methods have then been evaluated in a complete image coding-and-decoding algorithm. Simulation results show gains up to 2 dB in terms of the PSNR of the reconstructed signal after coding and decoding of the prediction residue when compared with H.264/AVC intraprediction modes, up to 3 dB when compared with template matching, and up to 1 dB when compared with a sparse prediction method.
authors: National Institute for Research in Computer Science and Control, INRIA/IRISA, Rennes, France. Mehmet.Turkan@gmail.com
authors: Mehmet
authors: M
authors: Türkan
authors: Christine
authors: C
authors: Guillemot
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique