Nonlinear processing of high-dimensional data is quite common in image filtering algorithms. Bilateral, joint bilateral, and non-local means filters are the examples of the same. Real-time implementation of high-dimensional filters has always been a research challenge due to its computational complexity. In this paper, we have proposed a solution utilizing both color sparseness and color dominance in an image which ensures a faster algorithm for generic high-dimensional filtering. The solution speeds up the filtering algorithm further by psycho-visual saliency-based deep encoded dominant color gamut, learned for different subject classes of images. The proposed bilateral filter has been proved to be efficient both in terms of psycho-visual quality and performance for edge-preserving smoothing and denoising of color images. The results demonstrate competitiveness of our proposed solution with the existing fast bilateral algorithms in terms of the CTQ (critical to quality) parameters.

高维数据的非线性处理在图像过滤算法中非常普遍。双边，联合双边和非本地均值过滤器就是它们的示例。高维滤波器的实时实现由于其计算复杂性，一直是研究的挑战。在本文中，我们提出了一种在图像中同时利用颜色稀疏性和颜色优势的解决方案，该解决方案确保了用于通用高维滤波的更快算法。该解决方案通过基于心理视觉显着性的深度编码主色域（进一步针对不同的图像主题类学习）进一步加快了过滤算法的速度。事实证明，所提出的双边滤波器在心理视觉质量和彩色图像边缘保持平滑和去噪性能方面都是有效的。