Video Error Concealment using Moment Invariance

Ghuge, Aditi D.; Rajani, P. K.; Khaparde, Arti

doi:10.1109/iccubea.2017.8463802

Cited by 3 publications

(1 citation statement)

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“…The basic idea behind EC is to predict the missing pixels by using the correctly received ones in the current frame or adjacent frames based on the spatial or temporal correlations. According to which kind of correlation is utilized, EC methods can be classified into three categories: spatial EC (SEC) [2][3][4][5][6][7][8][9][10][11][12][13][14][15], temporal EC (TEC) [16][17][18][19][20][21][22], or spatial-temporal EC (STEC) [23][24][25][26]. In the case that the neighbor frames are not available, the SEC methods only use the information extracted from the neighboring pixels of the missing ones in the current frame.…”

Section: Introductionmentioning

confidence: 99%

Image Error Concealment Based on Deep Neural Network

2019

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In this paper, we propose a novel spatial image error concealment (EC) method based on deep neural network. Considering that the natural images have local correlation and non-local self-similarity, we use the local information to predict the missing pixels and the non-local information to correct the predictions. The deep neural network we utilize can be divided into two parts: the prediction part and the auto-encoder (AE) part. The first part utilizes the local correlation among pixels to predict the missing ones. The second part extracts image features, which are used to collect similar samples from the whole image. In addition, a novel adaptive scan order based on the joint credibility of the support area and reconstruction is also proposed to alleviate the error propagation problem. The experimental results show that the proposed method can reconstruct corrupted images effectively and outperform the compared state-of-the-art methods in terms of objective and perceptual metrics.

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Section: Introductionmentioning

confidence: 99%

Image Error Concealment Based on Deep Neural Network

2019

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Image Error Concealment using Robust Data Hiding Technique

Ahamed

Sharma

Jain

2020

2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

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Spatial Error Concealment by Jointing Gauss Bayes Model and SVD for High Efficiency Video Coding

Zhou

Qin

Zhong

et al. 2019

Int. J. Patt. Recogn. Artif. Intell.

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This paper proposes a novel sparsity-based error concealment (EC) algorithm which integrates the Gauss Bayes model and singular value decomposition for high efficiency video coding (HEVC). Under the sequential recovery framework, pixels in missing blocks are successively reconstructed in Gauss Bayes mode. We find that the estimation error follows the Gaussian distribution in HEVC, so the error pixel estimation problem can be transferred to a Bayesian estimation. We utilize the singular value decomposition (SVD) technique to select sample pixels, which yields high estimation accuracy and reduces estimation error. A new recovery order based on confidence is established to resolve the error propagation problem. Compared to other state-of-the-art EC algorithms, experimental results show that the proposed method gives better reconstruction performance in terms of objective and subjective evaluations. It also has significantly lower complexity.

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Video Error Concealment using Moment Invariance

Cited by 3 publications

References 1 publication

Image Error Concealment Based on Deep Neural Network

Image Error Concealment Based on Deep Neural Network

Image Error Concealment using Robust Data Hiding Technique

Spatial Error Concealment by Jointing Gauss Bayes Model and SVD for High Efficiency Video Coding

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