Spatial Error Concealment Based on Edge Visual Clearness for Image/Video Communication

Koloda, Jan; Sánchez, Victoria; Peinado, Antonio M.

doi:10.1007/s00034-012-9504-0

Cited by 21 publications

(14 citation statements)

References 8 publications

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“…Figure 9 depicts the graph of the average PSNR over all videos versus the percentage of deleted blocks. We compared the proposed algorithm with other state-of-the-art [38], weighted template matching (WTM) [39], multivariate kernel density estimation (KMMSE) [40], and frequency selective extrapolation with residual filtering (XFSE) [41]. It can be observed that, as expected, the PSNR values decrease as the percentage of deleted blocks increases.…”

Section: Error Concealment For Imagesmentioning

confidence: 83%

Secure self-recovery watermarking scheme for error concealment and tampering detection

Freitas

Rigoni

2016

J Braz Comput Soc

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Section: Error Concealment For Imagesmentioning

confidence: 83%

Secure self-recovery watermarking scheme for error concealment and tampering detection

Freitas

Rigoni

2016

J Braz Comput Soc

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“…The expansion coefficient is estimated by minimizing the weighted energy from the last residual error:where ω ( x , y ) is a weighting function. It has been demonstrated that the influence of the weighting function decays symmetrically with distance from the center of the missing block (Koloda et al 2013). As a prior knowledge, the known sample in the vicinity of missing block has higher importance than the sample that is far from it, and the influence of the weighting function decreases with distance.…”

Section: Proposed Algorithm and Its Modelmentioning

confidence: 99%

“…When there is a similar patch, HEISI performs the exemplar-based inpainting; otherwise, it performs the spatial interpolation. The edge synthesis is also used in spatial error concealment, where multi-directional interpolations are combined according to the visual clearness (VC) of the edges (Koloda et al 2013). However, the VC algorithm is effective only when four neighboring blocks of a missing block are available, and it does not work for consecutive block loss.…”

Section: Introductionmentioning

confidence: 99%

Edge-aware spatial-frequency extrapolation for consecutive block loss

Líu

Wang

et al. 2016

SpringerPlus

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To improve the spatial error concealment (SEC) for consecutive block loss, an edge-aware spatial-frequency extrapolation (ESFE) algorithm and its edge-guided parametric model are proposed by selectively incorporating the Hough-based edge synthesis into the frequency-based extrapolation architecture. The dominant edges that cross the missing blocks are firstly identified by the Canny detector, and then the robust Hough transformation is utilized to systematically connect these discontinuous edges. During the generation of edge-guided parametric model, the synthesized edges are utilized to divide the missing blocks into the structure-preserving regions, and thus the residual error is reliably reduced. By successively minimizing the weighted residual error and updating the parametric model, the known samples are approximated by a set of basis functions which are distributed in a region containing both known and unknown samples. Compared with other state-of-the-art SEC algorithms, experimental results show that the proposed ESFE algorithm can achieve better reconstruction quality for consecutive block loss while keeping relatively moderate computational complexity.

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“…Although the blurred edges caused by stationary kernel functions are avoided, the hard division of missing block may introduce false borders. Multiple edges are also addressed in [29], where several directional interpolations are combined according to the visual clearness (VC) of the edges. In spite of its capability to reconstruct complicate edges, it is hard to accurately determine the location and the visual clearness of the edges.…”

Section: Introductionmentioning

confidence: 99%

Spatial Error Concealment With an Adaptive Linear Predictor

Liu

Zhai

Yang

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

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In this paper, a novel spatial error concealment algorithm is proposed. Under the sequential recovery framework, pixels in missing blocks are successively reconstructed based on adaptive linear predictor. The predictor automatically tunes its order and support shape according to local contexts. The predictor order and support shape are determined using Bayesian Information Criterion (BIC) which is able to strike a balance between the bias and variance of the prediction errors. The flexibility of the order-adaptive predictor is able to recover more important features or structures. A novel scan order based on the uncertainty of each pixel is also proposed to alleviate error propagation problem. Compared to the state-of-the-art error concealment algorithms, experimental results show that the proposed method gives better reconstruction performance in terms of objective and subjective evaluations.

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Spatial Error Concealment Based on Edge Visual Clearness for Image/Video Communication

Cited by 21 publications

References 8 publications

Secure self-recovery watermarking scheme for error concealment and tampering detection

Secure self-recovery watermarking scheme for error concealment and tampering detection

Edge-aware spatial-frequency extrapolation for consecutive block loss

Spatial Error Concealment With an Adaptive Linear Predictor

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