Motion adaptive field rate upconversion algorithms for 900 lines/100 Hz/2:1 displays

Gillies, Donald; Plantholt, M.; Westerkamp, D.

doi:10.1109/30.54282

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…In general, these methods can be roughly separated into two classes: single-field spatial domain methods, [3][4][5][6][7] and in-0091-3286/2011/$25.00 C 2011 SPIE terfield temporal domain methods, including motion adaptive methods 8,9 and motion compensation methods. 10,11 Although temporal domain methods perform better than spatial domain methods, they usually require higher hardware complexity.…”

Section: Introductionmentioning

confidence: 99%

Adaptive autoregressive deinterlacing method

Wu¹,

Huang

Jeon

et al. 2011

Opt. Eng

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This paper proposes a single-field deinterlacing method based on the autoregressive model and edge map. The new method interpolates missing pixels through estimating the deinterlaced covariance from the interlaced covariance, instead of estimating the edge orientations as previous intrafield deinterlaced methods (line average, edge-based lineaverage, direction-oriented interpolation, etc.) do. The proposed method adopts autoregressive mechanism, which considers mutual influence between the estimated missing pixels in a slip window. In addition, adding an edge map in our algorithm is used to reduce the computational complexity. The experimental results show that the proposed method outperformed the previous method in peak signal-to-noise ratio, and common artifacts (serration, line crawl, flicker, blurring, etc.) are significantly reduced. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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Section: Introductionmentioning

confidence: 99%

Adaptive autoregressive deinterlacing method

Wu¹,

Huang

Jeon

et al. 2011

Opt. Eng

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“…As well-known, Deinterlacing methods can be approximately classified into two categories: one is intra-field spatial domain method that only uses one field [3][4][5][6][7][8][9][10][11][12][13]; the other is an inter-field temporal 978-1-4673-2204-1/12/$31.00 ©2012 IEEE 468 deinterlacing method that uses multiple fields including motion adaptive methods and motion compensation (MC) methods [14][15][16][17]. The MC methods can provide flicker-free details in motion area, however, MC methods require high accuracy motion information, and in order to get this information, it will increase a large amount of hardware complexity.…”

Section: Introductionmentioning

confidence: 99%

Efficient adaptive intra-field deinterlacing algorithm using bilateral filter

Wang

Jeon

2012

2012 3rd IEEE International Conference on Network Infrastructure and Digital Content

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In this paper, we introduce an efficient adaptive intra-field deinterlacing framework using bilateral filter with consideration of pixel similarity between the interpolated pixel and the neighbor pixels. In the proposed framework, the low resolution image is first preprocessed using the simplest intra-field deinterlacing method, line average, due to its high efficiency and good performance, and then is decomposed using the fast bilateral filter into the detail and smooth layer which represent the small and large scale features, respectively. We process smooth layer and detail layer separately. For detail layer, instead of estimating the edge orientations with some candidate directions as previous intra-field deinterlacing methods, such as edge-based line average, efficient edge-based line average etc., we propose an adaptive but efficient deinterlacing method with consideration of pixel similarity, which avoids considering directional difference measures using limited candidate directions, resulting high efficiency. Compared with existing deinterlacing algorithms, the proposed algorithm improves peak signal-to-noise-ratio while maintaining high efficiency

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“…A third class of methods is given by so-called motion adaptive algorithms. These techniques respect the motion of objects during the interpolation process by locally switching between spatial and temporal information [2,8,17,22,28]. The type of information that is actually used for filling in the missing information is thereby determined by analyzing the local motion situation.…”

Section: Introductionmentioning

confidence: 99%

Deinterlacing with Motion-Compensated Anisotropic Diffusion

Ghodstinat

Bruhn

Weickert

2009

Lecture Notes in Computer Science

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Abstract. We present a novel deinterlacing scheme that makes consequent use of discontinuity-preserving partial differential equations (PDEs). It combines the accuracy of recent variational motion estimation techniques with the directional interpolation qualities of anisotropic diffusion filters. Our algorithm proceeds in three steps: First, we interpolate the interlaced images by means of a spatial edge enhancing diffusion process (EED). Then we apply the variational optic flow technique of Brox et al. (2004) in order to obtain a precise interframe registration. Finally we use a spatiotemporal generalisation of EED for motion-compensated inpainting of the missing data in the original sequence. Experiments demonstrate that the proposed method outperforms not only classical deinterlacing schemes, but also a recent PDE-based approach.

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Motion adaptive field rate upconversion algorithms for 900 lines/100 Hz/2:1 displays

Cited by 11 publications

References 7 publications

Adaptive autoregressive deinterlacing method

Adaptive autoregressive deinterlacing method

Efficient adaptive intra-field deinterlacing algorithm using bilateral filter

Deinterlacing with Motion-Compensated Anisotropic Diffusion

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