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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 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)
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 macro blocks are sequentially reconstructed by filling them with a weighted set of templates extracted from the available neighbourhood. Moreover, a fast(More)
This paper proposes a methodology for the application of multivariate kernel density estimation (KDE) to MMSE-based image/video error concealment (EC). We show that the estimation of the kernel bandwidth matrix for EC must follow a criterion different from that of typical KDE problems. In particular, we propose a bandwidth built as the product of a(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)
Digital images are commonly represented as regular 2D arrays, so pixels are organized in form of a matrix addressed by integers. However, there are many image processing operations, such as rotation or motion compensation, that produce pixels at non-integer positions. Typically, image reconstruction techniques cannot handle samples at non-integer positions.(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)
Summary form only given. In this paper, we propose a novel multi-mode error concealment algorithm that aims at obtaining high quality reconstructions with reduced computational burden. Block-based coding schemes in packet loss-environment are considered. The proposed technique exploits the excellent reconstructing abilities of the kernel-based minimum mean(More)