Spatial error concealment for H.264 using sequential directional interpolation

Kim, Myounghoon; Lee, Hoon Jae; Sull, Sanghoon

doi:10.1109/tce.2008.4711239

Cited by 24 publications

(7 citation statements)

References 10 publications

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“…The simple interpolation-based methods typically exploit the strong correlation among the neighboring blocks in the image [4], [33]- [36]. Although these methods are effective for real-time applications, in many cases, the quality of the concealed images is unsatisfactory in practice.…”

Section: B Error Concealmentmentioning

confidence: 99%

“…Then, the influences of the related parameters on the EC performance are evaluated in order to select the appropriate values for these parameters. Finally, the performance of the proposed JSR and JSR+NL algorithms, presented in Sections III and IV respectively, are evaluated via a suite of simulations carried out on a set of 8-bit grayscale standard images of size 512 × 512 pixels, selected from the CVG-Granada database 3 and Kodak dataset 4 . Note that 10 images, as shown in Fig.…”

Section: Experimental Studymentioning

confidence: 99%

“…In contrast to the transmitter-based methods, error concealment (EC), as a receiver-based method, is an alternative solution to mitigate the negative effects of the packet loss. The EC techniques recover the missing information without modifying the encoder or sending any additional information, leading to a better bandwidth efficiency [4]. These methods exploit the high spatial or temporal correlation existing among the lost areas and correctly received neighboring pixels to reconstruct a high quality image or video from its degraded version.…”

Section: Introductionmentioning

confidence: 99%

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Joint Sparse Learning With Nonlocal and Local Image Priors for Image Error Concealment

Akbari

Trocan

Sanei

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Joint sparse representation (JSR) model has recently emerged as a powerful technique with wide variety of applications. In this paper, the JSR model is extended to error concealment (EC) application, being effective to recover the original image from its corrupted version. This model is based on jointly learning a dictionary pair and two mapping matrices that are trained offline from external training images. Given the trained dictionaries and mappings, the restoration is done by transferring the recovery problem into the sparse representation domain with respect to the trained dictionaries, which is further transformed into a common space using the respective mapping matrices. Then, the reconstructed image is obtained by back projection into the spatial domain. In order to improve the accuracy and stability of the proposed JSR-based EC algorithm and avoid unexpected artifacts, the local and non-local priors are seamlessly integrated into the JSR model. The non-local prior is based on the self-similarity within natural images and helps to find an accurate sparse representation by taking a weighted average of similar areas throughout the image. The local prior is based on learning the local structural regularity of the natural images and helps to regularize the sparse representation, exploiting the strong correlation in the small local areas within the image. Compared with the state-of-the-art EC algorithms, the results show that the proposed method has better reconstruction performance in terms of objective and subjective evaluations.

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Section: B Error Concealmentmentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Sparse Learning With Nonlocal and Local Image Priors for Image Error Concealment

Akbari

Trocan

Sanei

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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“…The above algorithms, more or less ignore the edge information, and hence produce results that are smooth approximations of the missing blocks. On the other hand, directional SEC methods use edge related information [9,10,11,12,13]. Authors in [9] perform a projection onto a convex set after estimation of the most likely edge orientation.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian Process Regression Framework for Spatial Error Concealment with Adaptive Kernels

Asheri

Rabiee

Pourdamghani

et al. 2010

2010 20th International Conference on Pattern Recognition

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We have developed a Gaussian Process Regression method with adaptive kernels for concealment of the missing macro-blocks of block-based video compression schemes in a packet video system. Despite promising results, the proposed algorithm introduces a solid framework for further improvements. In this paper, the problem of estimating lost macro-blocks will be solved by estimating the proper covariance function of the Gaussian process defined over a region around the missing macro-blocks (i.e. its kernel function). In order to preserve block edges, the kernel is constructed adaptively by using the local edge related information. Moreover, we can achieve more improvements by local estimation of the kernel parameters. While restoring the prominent edges of the missing macro-blocks, the proposed method produces perceptually smooth concealed frames. Objective and subjective evaluations verify the effectiveness of the proposed method.

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“…Error concealment is a useful mechanism which conceals the received damaged data in the decoder side without increasing the redundancy of sending sequences and the required bandwidth. These techniques are divided into two main types: spatial domain [2][3][4][5] and temporal domain techniques.…”

mentioning

confidence: 99%

A Novel Boundary Matching Algorithm for Video Temporal Error Concealment

Marvasti-Zadeh¹,

Ghanei-Yakhdan²,

Kasaei³

2014

IJIGSP

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With the fast growth of communication networks, the video data transmission from these networks is extremely vulnerable. Error concealment is a technique to estimate the damaged data by employing the correctly received data at the decoder. In this paper, an efficient boundary matching algorithm for estimating damaged motion vectors (MVs) is proposed. The proposed algorithm performs error concealment for each damaged macro block (MB) according to the list of identified priority of each frame. It then uses a classic boundary matching criterion or the proposed boundary matching criterion adaptively to identify matching distortion in each boundary of candidate MB. Finally, the candidate MV with minimum distortion is selected as an MV of damaged MB and the list of priorities is updated. Experimental results show that the proposed algorithm improves both objective and subjective qualities of reconstructed frames without any significant increase in computational cost. The PSNR for test sequences in some frames is increased about 4.7, 4.5, and 4.4 dB compared to the classic boundary matching, directional boundary matching, and directional temporal boundary matching algorithm, respectively.Comment: arXiv admin note: text overlap with arXiv:1610.0738

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Spatial error concealment for H.264 using sequential directional interpolation

Cited by 24 publications

References 10 publications

Joint Sparse Learning With Nonlocal and Local Image Priors for Image Error Concealment

Joint Sparse Learning With Nonlocal and Local Image Priors for Image Error Concealment

A Gaussian Process Regression Framework for Spatial Error Concealment with Adaptive Kernels

A Novel Boundary Matching Algorithm for Video Temporal Error Concealment

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