An adaptive motion-compensated approach for video deinterlacing

Trocan, Maria; Mikovicova, Beata; Zhanguzin, Daulet

doi:10.1007/s11042-011-0845-7

Cited by 15 publications

(8 citation statements)

References 38 publications

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“…We also find from the available PSNR values in Table 5 that the proposed algorithm is superior to Trocan's algorithm. 22 As a result, the proposed algorithm outperforms the previous works in terms of objective visual quality of PSNR. In addition, Fig.…”

Section: Experimental Conditionmentioning

confidence: 81%

“…We compared the proposed algorithm with LA, ELA, Lee's algorithm, 9 Chang's algorithm, 13 Chen's algorithm, 14 Fan's algorithm, 17 Yang's algorithm, 18 Mohammadi's algorithm, 21 and Trocan's algorithm. 22 The number of reference fields for the proposed temporal deinterlacing was set to 3, i.e., the current field and its temporally previous and next fields.…”

Section: Experimental Conditionmentioning

confidence: 99%

“…1 We can categorize the existing algorithms into three groups as follows: spatial deinterlacing, [2][3][4][5][6][7][8] motion-adaptive (MA) deinterlacing, [9][10][11] and motion compensation (MC)-based deinterlacing. [12][13][14][15][16][17][18][19][20][21][22] Spatial deinterlacing algorithms have the advantages of simple operation and straightforward integration into hardware. The edge-based line averaging (ELA) algorithm is one of the most popular deinterlacing algorithms in this category.…”

Section: Introductionmentioning

confidence: 99%

“…[13][14][15][16][17][18][19][20][21][22] For example, Chang et al presented an adaptive four-field global/local motion-compensated approach where the same parity four-field motion detection and four-field motion estimation detect static areas and fast motion by four reference fields, and global motion estimation detects camera panning and zooming motions. 13 However, this algorithm may give rise to feathering artifacts when the assumption of strong continuity of local motion does not work.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal deinterlacing using a maximuma posterioriestimator based on multiple-field registration

Lee

et al. 2013

J. Electron. Imaging

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Abstract. This paper proposes an accurate deinterlacing algorithm using a maximum a posteriori (MAP) estimator. First, we produce accurate motion vector fields between the current field and adjacent fields by employing an advanced motion compensation scheme that is suitable for an interlaced format. Next, the progressive frame corresponding to the current field is found via the MAP estimator based on the derived motion vector fields. Here, in order to obtain a stable solution, well-known bilateral total variation-based regularization is applied. Then, at a specific mode decision step, it is decided whether the result from the aforementioned temporal deinterlacing is acceptable or not. Finally, if the temporal deinterlacing is determined to be inappropriate by the mode decision, a typical spatial deinterlacing is applied instead of the MAP estimator-based temporal deinterlacing. Experimental results show that the proposed algorithm provides at maximum 2 dB higher PSNR than a cutting-edge deinterlacing algorithm, while providing better visual quality than the latter. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Experimental Conditionmentioning

confidence: 81%

Section: Experimental Conditionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal deinterlacing using a maximuma posterioriestimator based on multiple-field registration

Lee

et al. 2013

J. Electron. Imaging

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“…However, motion-compensated deinterlacers need massive computational resources. To reduce their complexity, block-based motion estimation is used at the expense of blocking artifacts and some unreliable motion information [11], which severely degrades the visual quality of the reconstructed video sequences.…”

Section: Introductionmentioning

confidence: 99%

An HVS-inspired video deinterlacer based on visual saliency

Aggarwal

Trocan

Coudoux

2016

Vietnam J Comput Sci

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Video deinterlacing is a technique wherein the interlaced video format is converted into progressive scan format for nowadays display devices. In this paper, a spatial saliency-guided motion-compensated deinterlacing method is proposed which accounts for the properties of the Human Visual System (HVS): our algorithm classifies the field according to its texture and viewer's region of interest and adapts the motion estimation and compensation, as well as the saliency-guided interpolation to ensure high-quality frame reconstruction. Two different saliency models, namely the graph-based visual saliency (GBVS) model and the spectral residual visual saliency (SRVS) model, have been studied and compared in terms of visual quality performances as well as computational complexity. The experimental results on a great variety of video test sequences show significant improvement of reconstructed video quality with the GBVS-based proposed method compared to classical motion-compensated and adaptive deinterlacing techniques, with up to 4.5 dB gains in terms of PSNR. Simulations also show that the SRVS-based deinterlacing process can result to significant reductions of complexity (up to 25 times a decrease of the computation time compared with the GBVSbased method) at the expense of a PSNR decrease.

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