Fusion-based edge-sensitive interpolation method for deinterlacing
This paper proposes a fusion-based edge-sensitive interpolation method (FEID) for intra-field deinterlacing. The proposed FEID is composed of three steps: (1) region classification by a gradient-based region selection approach, (2) pre-interpolation by a 6-tap fixed coefficient Wiener filter, (3) da...
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| Veröffentlicht in: | Multimedia tools and applications 2015-09, Vol.74 (18), p.7643-7659 |
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| container_title | Multimedia tools and applications |
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| creator | Zhang, Hao Wang, Ruolin Liu, Wenjiang Rong, Mengtian |
| description | This paper proposes a fusion-based edge-sensitive interpolation method (FEID) for intra-field deinterlacing. The proposed FEID is composed of three steps: (1) region classification by a gradient-based region selection approach, (2) pre-interpolation by a 6-tap fixed coefficient Wiener filter, (3) data fusion by the linear minimum mean square-error estimation (LMMSE) technique. Specifically, three directional neighboring pixel sets are defined in three directions (45°, 90°, and 135°) for every missing pixel. And each set produces an estimate of the pixel to be interpolated with a Wiener filter. With the information that gathered from the three directional neighboring pixel sets, a more robust estimate is obtained by fusing these directional estimates with the LMMSE technique. For fast implementation, we propose a gradient-based region selection approach that classifies a local region into two different classes, Region 1 and Region 2. The LMMSE-based data fusion method is used in Region 1; a fast deinterlacing algorithm is used in Region 2 to reduce the computational complexity. Compared with existing deinterlacing methods, the proposed method FEID improves the visual quality of the interpolated edges while maintaining a higher peak signal-to-noise–ratio (PSNR) level. |
| doi_str_mv | 10.1007/s11042-014-1997-z |
| format | Article |
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The proposed FEID is composed of three steps: (1) region classification by a gradient-based region selection approach, (2) pre-interpolation by a 6-tap fixed coefficient Wiener filter, (3) data fusion by the linear minimum mean square-error estimation (LMMSE) technique. Specifically, three directional neighboring pixel sets are defined in three directions (45°, 90°, and 135°) for every missing pixel. And each set produces an estimate of the pixel to be interpolated with a Wiener filter. With the information that gathered from the three directional neighboring pixel sets, a more robust estimate is obtained by fusing these directional estimates with the LMMSE technique. For fast implementation, we propose a gradient-based region selection approach that classifies a local region into two different classes, Region 1 and Region 2. The LMMSE-based data fusion method is used in Region 1; a fast deinterlacing algorithm is used in Region 2 to reduce the computational complexity. 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The proposed FEID is composed of three steps: (1) region classification by a gradient-based region selection approach, (2) pre-interpolation by a 6-tap fixed coefficient Wiener filter, (3) data fusion by the linear minimum mean square-error estimation (LMMSE) technique. Specifically, three directional neighboring pixel sets are defined in three directions (45°, 90°, and 135°) for every missing pixel. And each set produces an estimate of the pixel to be interpolated with a Wiener filter. With the information that gathered from the three directional neighboring pixel sets, a more robust estimate is obtained by fusing these directional estimates with the LMMSE technique. For fast implementation, we propose a gradient-based region selection approach that classifies a local region into two different classes, Region 1 and Region 2. The LMMSE-based data fusion method is used in Region 1; a fast deinterlacing algorithm is used in Region 2 to reduce the computational complexity. Compared with existing deinterlacing methods, the proposed method FEID improves the visual quality of the interpolated edges while maintaining a higher peak signal-to-noise–ratio (PSNR) level.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Classification</subject><subject>Computer Communication Networks</subject><subject>Computer Science</subject><subject>Data fusion</subject><subject>Data integration</subject><subject>Data Structures and Information Theory</subject><subject>Digital video</subject><subject>Estimates</subject><subject>Interpolation</subject><subject>Methods</subject><subject>Multimedia</subject><subject>Multimedia computer applications</subject><subject>Multimedia Information Systems</subject><subject>Pixels</subject><subject>Signal to noise ratio</subject><subject>Special Purpose and Application-Based 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The proposed FEID is composed of three steps: (1) region classification by a gradient-based region selection approach, (2) pre-interpolation by a 6-tap fixed coefficient Wiener filter, (3) data fusion by the linear minimum mean square-error estimation (LMMSE) technique. Specifically, three directional neighboring pixel sets are defined in three directions (45°, 90°, and 135°) for every missing pixel. And each set produces an estimate of the pixel to be interpolated with a Wiener filter. With the information that gathered from the three directional neighboring pixel sets, a more robust estimate is obtained by fusing these directional estimates with the LMMSE technique. For fast implementation, we propose a gradient-based region selection approach that classifies a local region into two different classes, Region 1 and Region 2. The LMMSE-based data fusion method is used in Region 1; a fast deinterlacing algorithm is used in Region 2 to reduce the computational complexity. Compared with existing deinterlacing methods, the proposed method FEID improves the visual quality of the interpolated edges while maintaining a higher peak signal-to-noise–ratio (PSNR) level.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11042-014-1997-z</doi><tpages>17</tpages></addata></record> |
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| subjects | Algorithms Analysis Classification Computer Communication Networks Computer Science Data fusion Data integration Data Structures and Information Theory Digital video Estimates Interpolation Methods Multimedia Multimedia computer applications Multimedia Information Systems Pixels Signal to noise ratio Special Purpose and Application-Based Systems Studies |
| title | Fusion-based edge-sensitive interpolation method for deinterlacing |
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