We present a new image scaling method both for downscaling and upscaling, running with any scale factor or desired size. It is based on the sampling of an approximating bivariate polynomial, which globally interpolates the data and is defined by a filter of de la Vall\'ee Poussin type whose action ray is suitable regulated to improve the approximation. The method has been tested on a significant number of different image datasets. The results are evaluated in qualitative and quantitative terms and compared with other available competitive methods. The perceived quality of the resulting scaled images is such that important details are preserved, and the appearance of artifacts is low. Very high-quality measure values in downscaling and the competitive ones in upscaling evidence the effectiveness of the method. Good visual quality, limited computational effort, and moderate memory demanding make the method suitable for real-world applications.
翻译:我们提出了一个新的图像缩放缩放和升级方法,使用任何比例系数或理想大小运行。它基于一个近似双变多式模型的抽样,该模型在全球范围内对数据进行内插,并通过一个过滤器 " de la Vall\'ee Poussin 类型 " 的定义,其动作射线适合改进近似值。该方法在大量不同的图像数据集中进行了测试。结果用质量和数量术语评价,并与其他现有的竞争性方法进行比较。由此生成的缩放图像的可感知质量是保存重要细节,而文物的外观也很低。降缩放中的高质量计量值和提升该方法有效性的竞争性测量值非常高。良好的视觉质量、有限的计算努力和中度的记忆要求使该方法适合现实世界应用。