Intra Prediction Based on Markov Process Modeling of Images

In recent video coding standards, intraprediction of a block of pixels is performed by copying neighbor pixels of the block along an angular direction inside the block. Each block pixel is predicted from only one or few directionally aligned neighbor pixels of the block. Although this is a computationally efficient approach, it ignores potentially useful correlation of other neighbor pixels of the block. To use this correlation, a general linear prediction approach is proposed, where each block pixel is predicted using a weighted sum of all neighbor pixels of the block. The disadvantage of this approach is the increased complexity because of the large number of weights. In this paper, we propose an alternative approach to intraprediction, where we model image pixels with a Markov process. The Markov process model accounts for the ignored correlation in standard intraprediction methods, but uses few neighbor pixels and enables a computationally efficient recursive prediction algorithm. Compared with the general linear prediction approach that has a large number of independent weights, the Markov process modeling approach uses a much smaller number of independent parameters and thus offers significantly reduced memory or computation requirements, while achieving similar coding gains with offline computed parameters.


AKTIHANOGLU, M; OZGUC, B; AYKANAT, C (Springer Science and Business Media LLC, 1994-01-01)
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1-D Transforms for the Motion Compensation Residual
Kamışlı, Fatih (Institute of Electrical and Electronics Engineers (IEEE), 2011-04-01)
Transforms used in image coding are also commonly used to compress prediction residuals in video coding. Prediction residuals have different spatial characteristics from images, and it is useful to develop transforms that are adapted to prediction residuals. In this paper, we explore the differences between the characteristics of images and motion compensated prediction residuals by analyzing their local anisotropic characteristics and develop transforms adapted to the local anisotropic characteristics of t...
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Ferreira, Julio Cesar; Vural, Elif; Guillemot, Christine (Institute of Electrical and Electronics Engineers (IEEE), 2016-03-01)
Local learning of sparse image models has proved to be very effective to solve inverse problems in many computer vision applications. To learn such models, the data samples are often clustered using the K-means algorithm with the Euclidean distance as a dissimilarity metric. However, the Euclidean distance may not always be a good dissimilarity measure for comparing data samples lying on a manifold. In this paper, we propose two algorithms for determining a local subset of training samples from which a good...
Discretization of Parametrizable Signal Manifolds
Vural, Elif (Institute of Electrical and Electronics Engineers (IEEE), 2011-12-01)
Transformation-invariant analysis of signals often requires the computation of the distance from a test pattern to a transformation manifold. In particular, the estimation of the distances between a transformed query signal and several transformation manifolds representing different classes provides essential information for the classification of the signal. In many applications, the computation of the exact distance to the manifold is costly, whereas an efficient practical solution is the approximation of ...
Minimization of Monotonically Levelable Higher Order MRF Energies via Graph Cuts
Karci, Mehmet Haydar; Demirekler, Mübeccel (Institute of Electrical and Electronics Engineers (IEEE), 2010-11-01)
A feature of minimizing images of submodular binary Markov random field (MRF) energies is introduced. Using this novel feature, the collection of minimizing images of levels of higher order, monotonically levelable multilabel MRF energies is shown to constitute a monotone collection. This implies that these minimizing binary images can be combined to give minimizing images of the multilabel MRF energies. Thanks to the graph cuts framework, the mentioned class of binary MRF energies is known to be minimized ...
Citation Formats
F. Kamışlı, “Intra Prediction Based on Markov Process Modeling of Images,” IEEE TRANSACTIONS ON IMAGE PROCESSING, pp. 3916–3925, 2013, Accessed: 00, 2020. [Online]. Available: