IEEE Trans Image Process - Intra prediction based on Markov process modeling of images.

Tópicos

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Resumo

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.

Resumo Limpo

recent video code standard intrapredict block pixel perform copi neighbor pixel block along angular direct insid block block pixel predict one direct align neighbor pixel block although comput effici approach ignor potenti use correl neighbor pixel block use correl general linear predict approach propos block pixel predict use weight sum neighbor pixel block disadvantag approach increas complex larg number weight paper propos altern approach intrapredict model imag pixel markov process markov process model account ignor correl standard intrapredict method use neighbor pixel enabl comput effici recurs predict algorithm compar general linear predict approach larg number independ weight markov process model approach use much smaller number independ paramet thus offer signific reduc memori comput requir achiev similar code gain offlin comput paramet

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