Motion-compensated conversion from interlaced to progressive formats

Vandendorpe, Luc; Cuvilier, L.; Maison, B.; Queluz, Maria Paula; Delogne, Paul

doi:10.1016/0923-5965(94)90025-6

Cited by 35 publications

(19 citation statements)

References 19 publications

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“…Yen proved that any signal that is limited to a frequency of 0:5f s can be exactly reconstructed from N independent sets of samples, respresenting the same signal with a sampling frequency f s = N . This theorem can eectively be used to solve the problem of interpolation on a subsampled signal, as rst presented by Delogne [38] and Vandendorpe [39]. We shall call this method the GST (Generalized Sampling Theorem) de-interlacer method.…”

Section: 3mentioning

confidence: 99%

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Deinterlacing-an overview

1998

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Section: 3mentioning

confidence: 99%

“…A lter calculates the output sample. Appropriate lter coecients are derived in the papers of Delogne [38] and Vandendorpe [39]. Kalker [40] shows an alternative (algebraic) derivation which does not require Fourier transforms (see also [41]).…”

Section: 3mentioning

confidence: 99%

Deinterlacing-an overview

1998

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“…Vandendorpe et al 3 proposed a solution for motion estimation on interlaced video, using the generalised sampling theorem. This solution, which is illustrated in Figure 3a, is based on the assumption that the motion between two successive fields is uniform.…”

Section: Motion Estimation On Interlaced Video Applying the Generalismentioning

confidence: 99%

“…In this paper, we address the problem of correct motion estimation on interlaced material. Based on the Generalised Sampling Theorem (GST), a motion estimation criterion was proposed by Vandendorpe et al, 3 using three video input fields. This motion estimator minimises the difference between a GST prediction, using samples from the previous and pre-previous fields, and an existing pixel in the current field.…”

Section: Introductionmentioning

confidence: 99%

Motion estimation on interlaced video

Ciuhu

Haan

2005

SPIE Proceedings

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Motion compensated de-interlacing and motion estimation based on Yen's generalisation 1 of the sampling theorem (GST) have been proposed by Delogne 2 and Vandendorpe.3 Motion estimation methods using three-fields have been designed on a block-by-block basis, minimising the difference between two GST predictions. We will show that this criterion degenerates into a two-fields criterion, leading to erroneous motion vectors, when the vertical displacement per field period is an even number of pixels. We provide a solution for this problem, by adding a term to the matching criterion.

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“…It should be performed based on the results of MSA, with the assistance of motion information and texture information of each region. Generally speaking, MCI methods can be divided into three categories: motion adaptive MCI [8], recursive MCI [25], [26], and generalized sampling theorem (GST)-based MCI [27], [28]. Motion adaptive MCI algorithms take noise into account; however, the typical assumption that an obtained image equals to the sum of original image and noise is not valid in many practical cases [29].…”

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confidence: 99%

Deinterlacing Using Hierarchical Motion Analysis

Huang

Zhao

et al. 2010

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-A motion-compensated deinterlacing scheme based on hierarchical motion analysis is presented. According to deinterlacing steps, our contribution can be divided into four parts: motion estimation, motion state analysis, motion consistency analysis, and finer-grained interpolation. In motion estimation, we introduce a Gaussian noise model for choosing the best motion vector for each block, and make a tradeoff between utilizing previous de-interlaced frames and avoiding error propagation. A directional interpolation method is also introduced in this part for backward fields. In motion state analysis, we define two motion states for each pixel, thus achieve a compromise between traditional block-based strategies and the extreme pixel-based case. In motion consistency analysis, we propose to measure both the motion vector consistency and the motion state consistency in order to determine whether the previous two parts should be performed again with a different block size. In finer-grained interpolation, we utilize a combination of recursive median filters to generate the final results. Experimental results show that all of the proposed techniques are effective, either objectively or subjectively. As a result, we can achieve much higher image quality, with an average gain of about 1.83 dB in terms of peak signal-to-noise ratio. Moreover, the increased computation complexity is marginal.

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Motion-compensated conversion from interlaced to progressive formats

Cited by 35 publications

References 19 publications

Deinterlacing-an overview

Deinterlacing-an overview

Motion estimation on interlaced video

Deinterlacing Using Hierarchical Motion Analysis

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