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Novel intra deinterlacing algorithm using content adaptive interpolation
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AI
DOI：10.1109/TCE.2007.4341583.png)
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Abstract
En 中文
This paper presents a novel intra deinterlacing algorithm (NID) based on content adaptive interpolation. The NID consists of three steps: pre-processing, content classification, and content adaptive interpolation. There are also three main interpolation methods in our proposed NID, i.e. modified edge-based line averaging (M-ELA), gradient directed interpolation (GDI), and window matching method (WMM). Each proposed method shows different performances according to spatial local features. Therefore, we analyze the local region feature using the gradient detection and classify each missing pixel into four categories. And then, based on the classification result, a different de-interlacing algorithm is activated in order to obtain the best performance. Experimental results demonstrate that the NID method performs better than previous techniques.
Keywords:
deinterlacing
directional interpolation
ELA
gradient detection
window matching
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10.9
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5.1K
Citations:
6.8K
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