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<p>A summary is presented of a study on two-dimensional linear prediction models for image sequence processing and its application to change detection and scene coding. The study focused on two-dimensional joint process modeling of interframe relationships, the derivation of computationally efficient matching algorithms, and the implementation of a block-adaptive interframe predictor for use in interframe predictive coding and change detection. In the approach presented, the spatial nonstationarity is handled by an underlying quadtree segmentation structure. A maximum-likelihood criterion and a simpler minimum-variance criterion are discussed as detection and segmentation rules. The results of this research indicate that a constrained joint process model involving only a single gain parameter and a shift parameter is the best tradeoff between performance and computational complexity.</p>
picture processing; encoding; pattern recognition; image sequence processing; two-dimensional linear prediction models; change detection; scene coding; interframe relationships; block-adaptive interframe predictor; interframe predictive coding; spatial nonstationarity; quadtree segmentation structure; maximum-likelihood criterion; minimum-variance criterion; encoding; filtering and prediction theory; pattern recognition; picture processing; trees (mathematics)

P. Strobach, "Quadtree-Structured Linear Prediction Models for Image Sequence Processing," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 11, no. , pp. 742-748, 1989.
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