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Proceedings Paper

Adaptive error concealment with novel content classification
Author(s): Fangwen Fu; Lidong Xu; Xinggang Lin
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Paper Abstract

In recent years, various spatial error concealment techniques have been proposed only by partially considering the four following factors: 1) image continuity, 2) edge preservation, 3) texture recovery and 4) concealment complexity. Therefore, either the results of edge and texture recovery are unsatisfactory or the computation loan is too heavy to be acceptable. Aiming to overcome the above problems, a strap-based (strap means a set of consecutive macroblocks in horizon) framework, instead of block-based framework, is introduced in this paper. Within this framework, the content of each corrupted strap is classified into four classes: smooth area, edge area, low detail area and high detail area. Then a suitable method is selected for each class. Briefly speaking, bilinear interpolation, directional interpolation, best neighborhood match and Markov Random Field (MRF) model-based maximum a posterior (MAP) estimation are employed to conceal the above classes. During content classification, the gradient information of those pixels near the corrupted strap is calculated and presented as gradient points on a unit circle. The centroid, scatter degree and average gradient magnitude of those gradient points are calculated and used to classify the corrupted content. The results of our experiments demonstrate the efficiency of the proposed method and the impressive improvements in both objective and subjective measures have been achieved.

Paper Details

Date Published: 31 July 2006
PDF: 9 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59602Z (31 July 2006); doi: 10.1117/12.632646
Show Author Affiliations
Fangwen Fu, Tsinghua Univ. (China)
Lidong Xu, Tsinghua Univ. (China)
Xinggang Lin, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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