Spatiotemporal Selective Extrapolation for 3-D Signals and Its Applications in Video Communications

Meisinger, Katrin; Kaup, André

doi:10.1109/tip.2007.903261

Cited by 26 publications

(53 citation statements)

References 22 publications

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“…Although the algorithm has been introduced only for two-dimensional data sets, it can be extended straightforwardly to three dimensions by making use of the ideas from [23] and four dimensions by using [24]. There, a three-dimensional or, respectively, a four-dimensional model is generated in the same way as described above for two dimensions.…”

Section: Resultsmentioning

confidence: 99%

A Fast Algorithm for Selective Signal Extrapolation with Arbitrary Basis Functions

Seiler

Kaup

2011

EURASIP J. Adv. Signal Process.

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Signal extrapolation is an important task in digital signal processing for extending known signals into unknown areas. The Selective Extrapolation is a very effective algorithm to achieve this. Thereby, the extrapolation is obtained by generating a model of the signal to be extrapolated as weighted superposition of basis functions. Unfortunately, this algorithm is computationally very expensive and, up to now, efficient implementations exist only for basis function sets that emanate from discrete transforms. Within the scope of this contribution, a novel efficient solution for Selective Extrapolation is presented for utilization with arbitrary basis functions. The proposed algorithm mathematically behaves identically to the original Selective Extrapolation but is several decades faster. Furthermore, it is able to outperform existent fast transform domain algorithms which are limited to basis function sets that belong to the corresponding transform. With that, the novel algorithm allows for an efficient use of arbitrary basis functions, even if they are only numerically defined.

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Section: Resultsmentioning

confidence: 99%

A Fast Algorithm for Selective Signal Extrapolation with Arbitrary Basis Functions

Seiler

Kaup

2011

EURASIP J. Adv. Signal Process.

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“…Similar approaches are also known from image and video processing, e.g. [9]. Similarly to the speech enhancement algorithms mentioned above, the missing feature-type algorithms typically require very accurate detections of the keystroke transients.…”

Section: Introductionmentioning

confidence: 97%

An acoustic keystroke transient canceler for speech communication terminals using a semi-blind adaptive filter model

Buchner

Skoglund

Godsill

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In many teleconferencing applications using modern laptop and netbook devices it is common to encounter annoying keyboard typing noise. In this paper we propose an acoustic keystroke transient canceler for speech communication terminals as a novel broadband adaptive filter application in such a hands-free scenario. We present this approach in the context of the Google Chromebook Pixel device which is equipped with a special audio reference channel providing various new signal processing possibilities. Our novel semiblind/semi-supervised approach exploiting this new degree of freedom, combined with the system-based broadband estimation and a novel adaptation control yields a high-quality speech enhancement even under challenging acoustic conditions. Index Terms-Acoustic keystroke transients, noise reduction, impulsive noise, hands-free speech communication.

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“…They usually reconstruct the texture but not the structure. The work in [5] is an extension of [4] in which a spatio-temporal selective extrapolation strategy is used to reconstruct the missing area. Temporal techniques use available motion information to predict the missing motion vectors (MVs), for instance, by interpolating [6] or by selecting the MV that minimizes the side match distortion [7].…”

Section: Introductionmentioning

confidence: 99%

Inpainting-based error concealment for low-delay video communication

Aldahdooh

Barkowsky

Bull

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Error concealment (EC) is one of the target applications of inpainting techniques. Some methods combine the estimated lost motion vectors (MVs) with the exemplar-based inpainting technique to recover the lost regions. Due to the erroneous motion vectors that might indicate a moving object as background object and vice versa, these methods are still showing visual artifacts in the recovered regions. In this paper, a concept of motion map that can be easily generated in the decoder side is introduced and it is combined with the exemplarbased inpainting technique. The proposed method introduces an adaptive search window size that trades-off the quality and complexity. Moreover, an optional blending technique is proposed to limit the spatio-temporal artifacts. Experiments show that the proposed method improves the visual quality with 5dB on average relative to the state-of-the-art inpaintingbased EC method.

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Spatiotemporal Selective Extrapolation for 3-D Signals and Its Applications in Video Communications

Cited by 26 publications

References 22 publications

A Fast Algorithm for Selective Signal Extrapolation with Arbitrary Basis Functions

A Fast Algorithm for Selective Signal Extrapolation with Arbitrary Basis Functions

An acoustic keystroke transient canceler for speech communication terminals using a semi-blind adaptive filter model

Inpainting-based error concealment for low-delay video communication

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