Passive Error Concealment for Wavelet-Coded I-Frames With an Inhomogeneous Gauss–Markov Random Field Model

Abstract

In video communication over lossy packet networks (e.g., the Internet), packet loss errors can severely damage the transmitted video. The damaged video can largely be repaired with passive error concealment, where neighboring information is used to estimate missing information. We address the problem of passive error concealment for wavelet coded data with dispersive packetization. The reported techniques of this kind have many problems and usually fail in the reconstruction of high-frequency content. This paper presents a novel locally adaptive error concealment method for subband coded I-frames based on an inhomogeneous Gaussian Markov random field model. We estimate the parameters of this model from a local context of each lost coefficient, and we interpolate the lost coefficients accordingly. The results demonstrate a significant improvement over the reported related methods both in terms of objective performance measures and visually. The biggest improvement of the proposed method compared to the state-of-the-art in the field is the correct reconstruction of high-frequency information such as textures and edges.

Graph Learning-Based Ontology Sparse Vector Computing

“…The notation β i ∼ β j means there is an edge between vertices β i and β j in G β . The graph can be interpreted by means of the Gauss-Markov random fields (see Gleich and Datcu [18], Anandkumar et al [19], Rombaut et al [20], Fang and Li [21], Vats and Moura [22], Molina et al [23], and Borri et al [24] for more details on this topic and applications), the pairwise Markov property implies ¬β i ∼ β j equivalent to β i conditional independence to β j in V \ {β i , β j }. This property leads to the following choice of matrix Ξ:…”