Edge-based Line Average Research Articles

디인터레이싱을 위한 방향지향 보간법의 개선

ABSTRACT This paper presents, a deinterlacing method by improving the Direction-Oriented Interpolation (DOI) technique. The technique is considered to be a very strong tool for intrafield-based deinterlacing. However, DOI has some problems such as long processing time, wrong edge detection in periodic pattern. To remedy this problem, we replace the full search in DOI by a two-step search to reduce processing time and introduces two additional processes to improve image quality. In the proposed method, the spatial direction vectors (SDVs) misread data are reconsidered to prevent them utilizing in the next interpolation step, resulting in an accurate deinterlacing method. We conduct experiments with ISO experimental images to compare the proposed method with the existing methods including line evarage (LA), edge-based line averaging (ELA), DOI, selective deinterlacing algorithm (SDA). Expeimental results show the proposed method gives better performance in objective and subjective quality than existing deinterlacing methods.

Deinterlacing Using Taylor Series Expansion and Polynomial Regression

This paper introduces an efficient intra-field deinterlacing algorithm that is based on Taylor series expansion and polynomial regression. In order to estimate the value of an interpolated point using the given data, we rely on a generic local approximation function around this point for estimating the missing data. The well known $N$ -term Taylor series expansion is regarded as a local representation of the approximation function, and we use polynomial regression to find the optimal local approximation of the function. Instead of estimating the edge orientations as in previous intra-field deinterlacing methods, such as an edge-based line average, we propose an efficient deinterlacing method, which does not consider directional difference measurements that use limited candidate directions. When compared with existing deinterlacing algorithms, the proposed algorithm improves the peak signal-to-noise-ratio while maintaining a high efficiency.

향상된 움직임 탐색 기법을 적용한 움직임 적응적 디인터레이싱 알고리듬

이 논문에서는 향상된 움직임 적응적 디인터레이싱 알고리듬을 제안한다. 정확한 움직임 정보탐색을 위하여 세 가지 부분으로 구성된다 : (1) Modified Edge-based Line Average(MELA) 기법, (2) 연속된 5개의 필드에서의 움직임 탐색, 그리고 (3) 블록기반의 지역적 특성을 포함한다. 움직임 검출부분에서는 FIR 필터를 이용한다. 이것은 물체의 움직임의 내부를 정확하게 탐색 할 뿐만 아니라, 영상에 포함된 잡음도 줄일 수 있는 향상된 기법을 이용함으로써 계산 할 수 있다. 검출된 움직임의 정도에 따라서 가중치를 구하여 공간적 기법과 시간적 기법을 결합시킨다. 제안된 방법의 영상 시퀀스에 대한 실험 결과는 기존의 방법들에 비하여 주관적, 객관적인 비교에서 우수함을 보여준다. In this paper, a motion adaptive deinterlacing algorithm is proposed. It consists of three parts: (1) modified edge-based line average, (2) pixel-based consequent five-field motion detection, and (3) block-based local characteristic for detecting true motion and calculating the motion intensity by using an improved method which is able to detect the inner part of moving objects precisely as well as to reduce the risk of false detection caused by intrinsic noises in the image. Depending on the detected motion activity level, it combines spatial and temporal methods with weighting factor. Simulations conducted on several video sequences indicate that the performance of the proposed method is superior to the conventional methods in terms of both subjective and objective video quality.

Adaptive autoregressive deinterlacing method

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 line-average, 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.

지역 복잡도 기반 방법 선택을 이용한 적응적 디인터레이싱 알고리듬

In this paper, we propose an adaptive de-interlacing algorithm that is based on the degree of local complexity. The conventional intra field de-interlacing algorithms show the different performance according to the ways which find the edge direction. Furthermore, FDD (Fine Directional De-interlacing) algorithm has the better performance than other algorithms but the computational complexity of FDD algorithm is too high. In order to alleviate these problems, the proposed algorithm selects the most efficient de-interacing algorithm among LA (Line Average), MELA (Modified Edge-based Line Average), and LCID (Low-Complexity Interpolation Method for De-interlacing) algorithms which have low complexity and good performance. The proposed algorithm is trained by the DoLC (Degree of Local Complexity) for selection of the algorithms mentioned above. Simulation results show that the proposed algorithm not only has the low complexity but also performs better objective and subjective image quality performances compared with the conventional intra-field methods.

An Adaptive Deinterlacing Algorithm Using a Median Filter

In this paper, we propose a new deinterlacing method that converts the interlaced images into the progressive images using a field. Firsr of all, it estimates the direction of edge. If it makes an accurate estimate of the direction, then it interpolates a pixel using ELA(Edge-based Line Average). Otherwise, it estimates the new direction of edge, and then,, it interpolates a pixel using a proposed median filter. From simulation results, it is shown that the proposed method improves both subjective and objective image quality as compared with previous deinterlacing methods.

Hybrid video deinterlacing algorithm exploiting reverse motion estimation

This study proposes a new hybrid video deinterlacing algorithm method featuring a novel approach to qualify the reliability of motion vectors. The algorithm switches between motion-compensated and enhanced edge-based line averaging (ELA) methods based on motion vector reliability. When the motion vectors are calculated, reverse motion estimation (RME) is applied to the optimal matching block. A motion vector is assumed reliable if the result of RME refers to the original block or to a block in its vicinity. Motion compensation is used when motion vectors are reliable to improve the vertical resolution and enhanced ELA is used when the motion vectors are not reliable to prevent artefacts. Experimental results show that RME performs better than previous approaches, based on objective and subjective criteria. The computational complexity of the proposed method is up to two orders of magnitude less than previous methods, while the quality of the output compares well with the best previously reported methods.

A voting-based intra deinterlacing method for directional error correction

This paper presents a voting-based deinterlacing method for directional error correction (VDD), which can reduce wrong edge direction in conventional schemes. The VDD consists of four steps and their corresponding interpolation methods: a Filtered Modified- Edge-based Line Averaging (FM-ELA), a Majority Voting based Interpolation (MVI), a Majority voting-based Direction Averaging method (MDA), and the Direction-Oriented Interpolation (DOI). Each step can preserve edge directions and enhance visual quality in high spatial frequency regions through the analysis of local region features. After each missing pixel is categorized into one of three groups, a different deinterlacing technique is applied in order to provide the best result. Although it has a little bit higher complexity than previous methods, VDD can preserve delicate edges compared with previous methods. Experimental results show the improved subjective and objective quality of the reconstructed images.

Novel Intra Deinterlacing Algorithm Using Content Adaptive Interpolation

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.

Efficient deinterlacing algorithm using edge-based line average 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.