基于视觉信息保真度的图像增强质量客观评价方法

视觉信息保真度(VIF)是一种基于自然场景统计模型(NSS)、图像失真和人类视觉失真建模的新判据。传统上,图像质量评价算法将图像质量解释为使用"基准"或"完美"的图像作为参考的相似性或者保真度。本文将VIF方法应用于图像增强效果的评价,该方法将失真图像取代"完美"图像作为参考图像以评价增强后的图像的质量。由于VIF指标在某种程度上融合了HVS的特点,因此,相比传统方法具有明显的优势。本文通过大量的主观测试对该方法进行验证,实验显示该方法的性能优于当前的其他方法。

An Image Enhancement Quality Objective Assessment Method Based on Visual Information Fidelity

Visual information fidelity is a novel criterion that is based on modeling of natural scene statistics,image distortion and the human visual distortion.Traditionally,image QA algorithms interpret image quality as fidelity or similarity with a "reference" or "perfect" image.The VIF method is applied on assessing image enhancement effect which takes distorted image as "reference" image instead of "perfect" image to assess the quality of enhanced image.It provides clear advantages over the traditional approaches because VIF index is combined with HVS features under certain conditions.In particular,it can be measured only rely on the original image and enhanced image.We validate the performance of our method with an extensive subjective study to show that it outperforms current methods in our testing.