Adaptive De-interlacing Algorithm using Method Selection based on Degree of Local Complexity 


Vol. 36,  No. 4, pp. 217-225, Apr.  2011


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  Abstract

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.

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  Cite this article

[IEEE Style]

S. Hong, S. Park, J. Jeong, "Adaptive De-interlacing Algorithm using Method Selection based on Degree of Local Complexity," The Journal of Korean Institute of Communications and Information Sciences, vol. 36, no. 4, pp. 217-225, 2011. DOI: .

[ACM Style]

Sung-Min Hong, Sang-Jun Park, and Jechang Jeong. 2011. Adaptive De-interlacing Algorithm using Method Selection based on Degree of Local Complexity. The Journal of Korean Institute of Communications and Information Sciences, 36, 4, (2011), 217-225. DOI: .

[KICS Style]

Sung-Min Hong, Sang-Jun Park, Jechang Jeong, "Adaptive De-interlacing Algorithm using Method Selection based on Degree of Local Complexity," The Journal of Korean Institute of Communications and Information Sciences, vol. 36, no. 4, pp. 217-225, 4. 2011.