Abstract: In order to overcome the deficiency of the weighted average strategy which is adopted in the structure similarity algorithm for the perception of image quality, considering that certain regions in an image may not bear the same importance as others, an image quality assessment metric based on local invariant features was put forward. The algorithm used structural similarity to calculate the quality map of distorted image, and then extracted the local invariant features points in the distorted image. The region around features points was given more visual importance, and the quality of the image distortion could be evaluated by using integrated weighting strategy. The experimental results on the standard image library show that the computational complexity of this algorithm is relatively lower and the evaluation performance of structure similarity algorithm can be considerably increased, which achieves better consistency with the subjective assessment of human eyes.

Key words: image quality assessment, Structural Similarity(SSIM), Scale Invariant Features Transform(SIFT), visual importance, Human Visual System

摘要： 针对结构相似度算法在感知图像质量时采取平均加权策略的不足，利用人眼对图像中不同区域的关注程度不同的特性，提出了基于局部不变特征的图像质量评价算法。该算法在失真图像结构相似度质量分布图的基础上，提取图像的局部不变特征点，将这些特征点周围一定区域赋予较大的视觉权重，最后运用综合加权策略来衡量失真图像的质量。在标准图像测试库上的实验结果表明，该算法计算复杂度相对较低，较大地提高了结构相似度算法的评价效果，与人眼主观感知图像质量取得了更好的一致性。

关键词: 图像质量评价, 结构相似度, 尺度不变特征变换, 视觉重要性, 人眼视觉系统