A new direction-oriented interpolation method is proposed and then applied to de-interlacing. The proposed method introduces the upper spatial direction vector. (USDV) and the lower spatial direction vector (LSDV) to obtain more accurate direction of the highest spatial correlation. Using the USDV and the LSDV, the proposed method reduces possibility of wrong decision for the highest-correlated spatial direction. Line-based directional interpolation is performed after the direction vector is found. Extensive simulations conducted for images and video sequences show the efficacy of the proposed method over the previous methods based on the ELA and over the line averaging method in terms of the objective and subjective image quality.

Direction-oriented interpolation and its application to de-interlacing

A motion adaptive deinterlacing algorithm is presented in this paper. It consists of the ELA-median directional interpolation, same-parity 4-field horizontal motion detection, morphological operation for noise reduction and adaptive threshold adjusting. The edges can be sharper when the ELA-median directional interpolation is adopted. The same-parity 4-field horizontal motion detection detects faster motion and makes more accurate determinations about where objects are going to move. The morphological operation for noise reduction and adaptive threshold adjusting preserve the actual texture of the original objects. The proposed method achieves cost-efficient hardware implementation with low complexity, low memory usage, and high-speed processing capability. In addition, it consumes less time in producing high-quality images and allows the audience to enjoy a high-quality TV sequence on their progressive devices. The experimental results show that the proposed algorithm is more cost-effective than previous systems.

/pdf/motion-adaptive-interpolation-with-horizontal-motion-hhamstcs89.pdf

Motion adaptive interpolation with horizontal motion detection for deinterlacing

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.

Novel Intra Deinterlacing Algorithm Using Content Adaptive Interpolation

An efficient algorithm is proposed for the interpolation of interlaced images. On the basis of the edge-based line average (ELA) algorithm, tow useful measurements are introduced within the operation window in order to alleviate misleading decisions in determining the direction where the interpolation is to be made. By efficiently estimating the directional spatial correlations of neighboring pixels, increased interpolation accuracy has been achieved. In addition, the new method is simply structured and is therefore easy to implement. Extensive simulations conducted for different images and video sequences have shown the efficacy of the proposed interpolator, with significant improvement over previous ELA-based algorithms.

Efficient deinterlacing algorithm using edge-based line average interpolation

This paper presents an intra field de-interlacing algorithm for spatial edge preserving using the detection of accurate edge direction. Conventional intra field de-interlacing algorithms determine the edge direction at the pixel or half pixel level, so that they can be highly sensitive to noise and lead to image degradation. In this paper, the proposed algorithm first considers the edge tendency in the edge region, and then candidate direction vectors (CDVs) are selected using the modified Sobel operation through the edge tendency. Finally, the CDVs are adaptively applied to interpolate the lost pixel in the edge region. Experimental results show that the proposed algorithm performs well with a variety of still and moving images compared with conventional intra field algorithms in the literature.

Fine directional de-interlacing algorithm using modified Sobel operation

An adaptive technique for scanning rate conversion and interpolation is proposed. This technique performs better than the edge-based line average algorithm, especially for an image with more horizontal edges. Moreover, it is easy to implement and a simple VLSI architecture is proposed. Computer simulation shows that a 37.0 dB image can be obtained via our proposed technique, while edge-based line average algorithm only achieves 35.2 dB.

Adaptive interpolation technique for scanning rate conversion

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Adaptive interpolation technique for scanning rate conversion