Cubic Convolution Interpolation Research Articles

Low-complexity direct computation algorithm for cubic-spline interpolation scheme

Cubic-spline interpolation (CSI) scheme is known to be designed to resample the discrete image data based on the least-square method in conjunction with the cubic convolution interpolation (CCI) function. It is superior in performance and can be used together with the discrete cosine transform (DCT)-based image or video codec to improve the coding performance for a variety of high compression ratios. In this paper, we firstly make some comments on the direct computation algorithm for CSI scheme developed by Lin et al. Moreover, a low-complexity direct computation algorithm for CSI scheme is developed to further improve the computational efficiency. The mathematical derivations and simulation results indicate that such simplified CSI scheme using the proposed low-complexity direct computation algorithm can achieve almost the same objective and subjective performance with much fewer arithmetic operations in comparison with the CSI scheme using the direct computation algorithm.

Mathematical analysis for CSI scheme with the interpolation kernel size increased

The cubic-spline interpolation (CSI) scheme is known to be designed to resample the discrete image data based on the least-square method in conjunction with the cubic convolution interpolation (CCI) function. In this CSI scheme, the improved quality of resampling can be achieved as the interpolation kernel size increases. However, the improvement of the performance gets less and less. This means that the performance of the CSI scheme has not been significantly improved and converges toward a constant value when the interpolation kernel size exceeds a certain value. A proof of this result is given in this study and has never been seen in the literature to the authors' knowledge. Moreover, this study analyses the relationship between the performance and computational complexity of the CSI schemes with different interpolation kernel sizes and compares them from a structural point of view. Simulation results indicate that it is in agreement with the theoretic derivations. Since the arithmetic operations required are increasing linearly with the increment of the interpolation kernel size, selecting an interpolation kernel size gives the best trade-off between the performance and computational complexity in practical applications. However, the optimum choice of the interpolation kernel size depends crucially on effective demand.

An Efficient Fractional-Pixel Motion Compensation Based on Cubic Convolution Interpolation

The fractional-pixel motion compensation is used in the H.264/AVC algorithm, in order to improve the coding efficiency of fractional-pixel displacement, an efficient cubic convolution interpolation (CCI) with four coefficients is proposed. In this paper, the detailed derivation of the CCI filter and using CCI with fractional-pixel displacement are presented. It is shown by computer simulation that the presented method substantially reduces the computation complexity and also increases the precision of the motion compensation.

Novel Approaches to the Parametric Cubic-Spline Interpolation

The cubic-spline interpolation (CSI) scheme can be utilized to obtain a better quality reconstructed image. It is based on the least-squares method with cubic convolution interpolation (CCI) function. Within the parametric CSI scheme, it is difficult to determine the optimal parameter for various target images. In this paper, a novel method involving the concept of opportunity costs is proposed to identify the most suitable parameter for the CCI function needed in the CSI scheme. It is shown that such an optimal four-point CCI function in conjunction with the least-squares method can achieve a better performance with the same arithmetic operations in comparison with the existing CSI algorithm. In addition, experimental results show that the optimal six-point CSI scheme together with cross-zonal filter is superior in performance to the optimal four-point CSI scheme without increasing the computational complexity.

Image zooming using directional cubic convolution interpolation

Image-zooming is a technique of producing a high-resolution image from its low-resolution counterpart. It is also called image interpolation because it is usually implemented by interpolation. Keys’ cubic convolution (CC) interpolation method has become a standard in the image interpolation field, but CC interpolates indiscriminately the missing pixels in the horizontal or vertical direction and typically incurs blurring, blocking, ringing or other artefacts. In this study, the authors propose a novel edge-directed CC interpolation scheme which can adapt to the varying edge structures of images. The authors also give an estimation method of the strong edge for a missing pixel location, which guides the interpolation for the missing pixel. The authors’ method can preserve the sharp edges and details of images with notable suppression of the artefacts that usually occur with CC interpolation. The experiment results demonstrate that the authors'method outperforms significantly CC interpolation in terms of both subjective and objective measures.