Spatial error concealment of corrupted image data using frequency selective extrapolation

Meisinger, Katrin; Kaup, André

doi:10.1109/icassp.2004.1326518

Cited by 26 publications

(20 citation statements)

References 6 publications

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“…In case of concealment, the isotropic weighting function gains severable dBs in PSNR [14] with respect to a binary weighting function used in [13]. The concealment performance decreases if there are details in the support area which do not belong to the missing area.…”

Section: Block Coded Datamentioning

confidence: 97%

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Frequency selective signal extrapolation with applications to error concealment in image communication

Kaup

Meisinger

Aach

2005

AEU - International Journal of Electronics and Communications

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Section: Block Coded Datamentioning

confidence: 97%

“…Two-dimensional DCT basis functions contain only horizontal and vertical structures whereas the DFT has also diagonal basis images being therefore better suited for the signal extrapolation [13].…”

Section: Frequency Selective Extrapolation Using 2d Dft Basis Functionsmentioning

confidence: 99%

Frequency selective signal extrapolation with applications to error concealment in image communication

Kaup

Meisinger

Aach

2005

AEU - International Journal of Electronics and Communications

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“…Signal reconstruction and error concealment methods, however, as opposed to bit-to-bit error correction, consist in obtaining a close approximation of the original image, using error-free neighboring information to perform interpolation onto damaged areas, in either spectral or spatial domains [23,24,26,27,30,31,33,38,40,[42][43][44]48,49,[57][58][59].…”

Section: Article In Pressmentioning

confidence: 99%

A multi-resolution, geometry-driven error concealment method for corrupted JPEG color images

Bourdon¹,

Augereau²,

Châtellier³

et al. 2005

Signal Processing: Image Communication

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“…For selection of the domain in which image is interpolated, the common choices vary from direct interpolations over raw spatial domain pixels [8,18], to methods which operate on transformation of images using discrete cosine transform (DCT) [7] or discrete wavelet transforms (DWT) [17]. EC methods using combination of spatial and frequency domains [20,21,26].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, EC methods can be coded as stand-alone apps and deployed in networks or used as embedded applications on the receiver handsets/terminals; they do not require an international telecommunication union (ITU) approved standard. Therefore, spatial EC image gap restoration is the category of solution explored in this paper.At their core, image EC computation algorithms often involve two distinct processes: (a) image transformation and (b) extrapolation or interpolation of missing gaps.For selection of the domain in which image is interpolated, the common choices vary from direct interpolations over raw spatial domain pixels [8,18], to methods which operate on transformation of images using discrete cosine transform (DCT) [7] or discrete wavelet transforms (DWT) [17]. EC methods using combination of spatial and frequency domains [20,21,26].…”

mentioning

confidence: 99%

Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation

Langari

Vaseghi

Procházka

et al. 2016

IEEE Trans. on Image Process.

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-This paper presents improvements in image gap restoration through incorporation of edge-based directional interpolation within multi-scale pyramid transforms. Two types of image edges are reconstructed; (a) the local edges or textures, inferred from the gradients of the neighbouring pixels and (b) the global edges between image objects or segments, inferred using Canny detector. Through a process of pyramid transformation and downsampling, the image is progressively transformed into a series of reduced size layers until at the pyramid apex the gap size is one sample. At each layer an edge 'skeleton' image is extracted for edge-guided interpolation. The process is then reversed; from the apex, at each layer, the missing samples are estimated (an iterative method is used in the last stage of up-sampling), up-sampled and combined with the available samples of the next layer. Discrete cosine transform and a family of discrete wavelet transforms are utilized as alternatives for pyramid construction. Evaluations over a range of images, in regular and random loss pattern, at loss rates of up to 40%, demonstrate that the proposed method improves PSNR by 1 to 5 dB compared to a range of best published works.Index Terms-Error concealment, multi-scale DCT/DWT pyramid, edge detection, image gap recovery, packet loss concealment. I. INTRODUCTIONmage gap restoration have a wide range of applications that includes in-painting of missing or damaged segments in still images or the replacement of image data packet lost in transmission. Further examples of applications and environments where image gap restoration can be usefully applied are enhancement of distorted biomedical signals [1], restoration of archived damaged images [2] and packet loss concealment over internet protocol (IP) networks [3].A main current application of image gap restoration is packet loss concealment. Packet loss errors may occur due to network congestions or due to signal loss in mobile devices. IP networks are best-effort environments [4,5] where the packet delivery is not guaranteed. The rapid growth in demand for relatively high bandwidth image/video streaming applications over IP networks motivates the need for packet loss recovery and concealment in order to provide more reliable network services and more acceptable user experience [6].There are three broad approaches for mitigating the loss of quality in received images due to packet loss: (a) automatic request for retransmission (ARQ) of the lost packets, (b) error control via forward error correction (FEC) methods and (c) error concealment (EC) methods. The first method retransmits a copy of the damaged/lost packet and results in an increase in bandwidth and delay proportional to error rate [5]. This method can be used on request for retransmission in networks where there is an interaction between sender and receiver. The second category of methods, FEC, employs error correction coding to recover lost pixels from the received information. This implies that the pixel values in successive...

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Spatial error concealment of corrupted image data using frequency selective extrapolation

Cited by 26 publications

References 6 publications

Frequency selective signal extrapolation with applications to error concealment in image communication

Frequency selective signal extrapolation with applications to error concealment in image communication

A multi-resolution, geometry-driven error concealment method for corrupted JPEG color images

Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation

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