A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

Lee, Gwo Giun; Wang, Ming-Jiun; Li, Hsin-Te; Lin, He-Yuan

doi:10.1155/2008/741290

Cited by 16 publications

(10 citation statements)

References 13 publications

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“…• Content adaptive / characterization of different image regions / detection of artifacts [64][65][66][67][68] • Texture detection and classification 64, 68-72…”

Section: Related Workmentioning

confidence: 99%

High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks

Müller

2016

SPIE Proceedings

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The necessity to process interlaced images in surveillance, reconnaissance, or further computer vision areas should be a topic of the past. But, for different reasons, it is not. So, there are situations in practice, in which interlaced images have to be processed. Since a lot of algorithms strongly degrade when working with such images directly, a usual method is to double or interpolate image lines in order to discard one of the two enclosed image frames. This is efficient but leads to weak results, in which half of the original information is lost. Alternatively, a lot of valuable computation time has to be spent to solve the highly complex motion compensation task in order to improve the results significantly. In this paper, an efficient algorithm is presented to solve this dilemma. First, the algorithm solves the complex 2-D mapping problem using the best state-of-the art optical flow method that could be found for this purpose. But, of course, for different physical reasons there are regions which cannot properly be handled by optical flow by itself. Therefore, an efficient post-processing method detects and removes remaining artifacts afterwards, which is the main contribution of this paper. This method is based on color interpolation incorporating local image structure. The presented results document the overall performance of the approach with respect to obtained image quality and calculation time. The method is easy to implement and offers a valuable pre-processing for a lot of computer vision tasks

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“…• Content adaptive / characterization of different image regions / detection of artifacts [64][65][66][67][68] • Texture detection and classification 64, 68-72…”

Section: Related Workmentioning

confidence: 99%

High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks

Müller

2016

SPIE Proceedings

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“…1 ranks the spectrum of visual computing algorithms based on qualitative complexity analysis. 1 Extrapolating from a high-level description, as illustrated in Fig. 1, future visual computing algorithms will have better content adaptivity, extended use of temporal information, and further increase the physical size and resolution of the image sequences as they continuously deliver better visual quality.…”

Section: Algorithm/architecture Co-exploration Of Visualmentioning

confidence: 99%

“…Similarly, the complexity of video processing algorithms, such as motion adaptive deinterlacers [1], [2], scan-rate converters, and other format converters, is also characterized by studying the three complexity features discussed above. Content-adaptive algorithms for high definition video processing, such as those in scalers, deinterlacers, etc., which are based on the texture contents from surrounding neighbors, have also been documented in the literature [3], [4] and used in many applications.…”

Section: Algorithm/architecture Co-exploration Of Visualmentioning

confidence: 99%

Algorithm/Architecture Co-Exploration of Visual Computing on Emergent Platforms: Overview and Future Prospects

Lee

Chen

Mattavelli

et al. 2009

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Concurrently exploring both algorithmic and architectural optimizations is a new design paradigm. This survey paper addresses the latest research and future perspectives on the simultaneous development of video coding, processing, and computing algorithms with emerging platforms that have multiple cores and reconfigurable architecture. As the algorithms in forthcoming visual systems become increasingly complex, many applications must have different profiles with different levels of performance. Hence, with expectations that the visual experience in the future will become continuously better, it is critical that advanced platforms provide higher performance, better flexibility, and lower power consumption. To achieve these goals, algorithm and architecture co-design is significant for characterizing the algorithmic complexity used to optimize targeted architecture. This paper shows that seamless weaving of the development of previously autonomous visual computing algorithms and multicore or reconfigurable architectures will unavoidably become the leading trend in the future of video technology.

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“…Motion analysis consists of motion detection and motion consistency check. The motion detector in [20] is employed in knowing whether the temporal neighbor is reliable for interpolation. The motion detector in [20] detects both fast and slow motion with high accuracy.…”

Section: Algorithmic Design Of Deinterlacingmentioning

confidence: 99%

“…The motion detector in [20] is employed in knowing whether the temporal neighbor is reliable for interpolation. The motion detector in [20] detects both fast and slow motion with high accuracy. Moreover, the spatial correlation and temporal history are utilized for consistency check.…”

Section: Algorithmic Design Of Deinterlacingmentioning

confidence: 99%

Reconfigurable Architecture for Deinterlacer based on Algorithm/Architecture Co-Design

Lee

Wang

Chen

et al. 2009

J Sign Process Syst

Self Cite

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This paper presents the algorithm and reconfigurable architecture of motion-adaptive deinterlacer for highdefinition video. The content-adaptability of algorithm and the reconfiguration of architecture are concurrently explored by algorithm/architecture co-design methodology and Caltrop actor language (CAL) modeling of the dataflow. In the design methodology we employed, the CAL dataflow model is also very helpful in the verification of our deinerlacer. The proposed algorithm and architecture design of deinterlacer is more cost-efficient than two recently proposed works in terms of algorithmic performance and silicon area of VLSI implementation. Moreover, data path reconfiguration efficiently enables various interpolation schemes using less computational resource of hardware than non-reconfigurable architecture.

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A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

Cited by 16 publications

References 13 publications

High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks

High-performance image deinterlacing using optical flow and artifact post-processing on GPU/CPU for surveillance and reconnaissance tasks

Algorithm/Architecture Co-Exploration of Visual Computing on Emergent Platforms: Overview and Future Prospects

Reconfigurable Architecture for Deinterlacer based on Algorithm/Architecture Co-Design

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