Ján Koloda

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—In this paper we propose a novel sequential error concealment algorithm for video and images based on sparse linear prediction. Block-based coding schemes in packet loss environment are considered. Images are modelled by means of linear prediction and missing macroblocks are sequentially reconstructed using the available groups of pixels. The optimal(More)
In this paper we propose a novel spatial error concealment algorithm for video and images based on convex optimization. Block-based coding schemes in packet loss environment are considered. Missing macroblocks are sequentially reconstructed by filling them with a weighted set of templates extracted from the available neighbourhood. Moreover, a fast(More)
In this paper, we propose a technique for concealing missing image/video blocks based on the concept of visual clearness of an edge. A scanning procedure based on the Hough transform allows us to find the relevant edges, and the visually clearest ones are employed in an interpolation based reconstruction. Specifically, several interpolations are combined(More)
The purpose of signal extrapolation is to estimate unknown signal parts from known samples. This task is especially important for error concealment in image and video communication. For obtaining a high quality reconstruction, assumptions have to be made about the underlying signal in order to solve this underdetermined problem. Among existent(More)
This paper proposes a new variant of the least square autore-gressive (LSAR) method for speech reconstruction, which can estimate via least squares a segment of missing samples by applying the linear prediction (LP) model of speech. First, we show that the use of a single high-order linear predictor can provide better results than the classic LSAR(More)
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