2011Unpublished venueRequires access

Fast algorithm of modified cubic convolution interpolation

Jin-tai Shangguan, Yanling Li, Yonggang Wang, Huiling Li

Open publisher page 8 citations

Abstract

Cubic convolution interpolation algorithm is one of the most widely used image interpolation methods. Compared with the linear interpolation, it has a more accurate result, but a lower interpolation speed due to its computational complexity. The cubic convolution algorithm needs sixteen points in two-dimensional image interpolation. The new algorithm proposed in this paper reduces the necessary points to nine by using the correlation among neighboring pixels. Furthermore, the times of multiplication and addition involved in interpolation can be reduced to 53% and 48% respectively of those by original cubic convolution. So the new algorithm achieved lower computational complexity and a higher interpolation speed.

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What this paper is about

Cubic convolution interpolation algorithm is one of the most widely used image interpolation methods. Compared with the linear interpolation, it has a more accurate result, but a lower interpolation speed due to its computational complexity. The cubic convolution algorithm needs sixteen points in two-dimensional image interpolation. The new algorithm proposed in this paper reduces the necessary points to nine by using the correlation among neighboring pixels. Furthermore, the times of multiplication and addition involved in interpolation can be reduced to 53% and 48% respectively of those by original cubic convolution. So the new algorithm achieved lower computational complexity and a higher interpolation speed.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Cubic convolution interpolation algorithm is one of the most widely used image interpolation methods. Compared with the linear interpolation, it has a more accurate result, but a lower interpolation speed due to its computational complexity. The cubic convolution algorithm needs sixteen points in two-dimensional image interpolation. The new algorithm proposed in this paper reduces the necessary points to nine by using the correlation among neighboring pixels. Furthermore, the times of multiplication and addition involved in interpolation can be reduced to 53% and 48% respectively of those by original cubic convolution. So the new algorithm achieved lower computational complexity and a higher interpolation speed.

Key concepts: Interpolation (computer graphics), Convolution (computer science), Bicubic interpolation, Stairstep interpolation, Algorithm, Linear interpolation, Nearest-neighbor interpolation, Bilinear interpolation

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