Biquadratic B-spline local interpolation-based image scaling method

Abstract

The invention discloses a biquadratic B-spline local interpolation-based image scaling algorithm. The core of the algorithm is to perform interpolation description on 16 pixels in a local 4*4 area of a source image by adopting a biquadratic B-spline function, and realizes the scaling of an image by performing resampling on the function. The algorithm avoids the problem of phase distortion caused by the conventional quadratic interpolation method, and can achieve an effect similar to a bicubic algorithm on vision effect. The algorithm is a localized algorithm, each processing unit and the utilized pixel resource are mutually independent, and the processing processes thereof have very good parallelism, so the algorithm can be accelerated in a CPU. Therefore, the efficiency of the algorithm is improved on the basis of ensuring the effect of the bicubic algorithm, and the real-time requirement on image processing is met.

Description

technical field [0001] The invention relates to the fields of computer image processing and image scaling, in particular to an image scaling method based on biquadratic B-spline local interpolation. Background technique [0002] Image scaling is to change the resolution of the image, including image enlargement from low resolution to high resolution and image reduction from high resolution to low resolution. This feature reconstructs the pixel color value of the target image at the new resolution. Therefore, image scaling can also be understood as resampling in image reconstruction. [0003] There are many image scaling algorithms, the most common one is the interpolation algorithm based on kernel function, including zero-order interpolation, linear interpolation and various improved cubic interpolation functions, but the quadratic interpolation function is rarely mentioned. According to Schafer and Rabiner (cf. Thomas M. Lehmann, Claudia Klaus Spitzer. Survey: Interpola...

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Problems solved by technology

According to the theoretical analysis, the quadratic interpolation mainly has the following problems: first, the quadratic interpolation function for interpolating three sampling points is not a linear shift invariant system, which will cause image distortion and cause bad visual effects when used for image scaling; The quadratic function only uses three sampling points in the interpolation process, which will result in an asymmetrical situation where two sampling points are located on one side of the current requested point and one sampling point is located on the other side

[0004] The zero-order interpolation and bilinear interpolation algorithms widely used in the early days are simple and efficient, but they will produce obvious jagged or blurred edges; cubic interpolation improves the disadvantages of edge blur in low-order interpolation to a certain extent, but the calculation The amount is large, which reduces the computational efficiency

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