基于视觉相似性的半色调图像评价方法

摘要

To measure quality of halftones or halftoning algorithms, this paper proposed Mean Luminance Similarity Error (MLSE), Mean Contrast Similarity Error ( MCSE) and Visual Perception Similarity Error ( VPSE) to measure the visual perception similarity between halftones and their original continuous-tone images. In the proposed method, an input image was divided into several blocks according to local adaptation of human eyes, and luminance similarity error and contrast similarity error in each block were computed according to luminance adaptation and contrast masking on visual perception. Finally, the MLSE, MCSE and VPSE were computed to evaluate luminance similarity, texture similarity and local unexpected artifacts respectively. The experimental results show that the proposed quality measures are more agreeable to HVS in comparison with Peak Signal to Noise Ratio ( PSNR) and Weighted Signal to Noise Ratio ( WSNR) in terms of luminance similarity and in comparison with Unique Quality Index (QUI) and Structure Similarity Index Measure (SSIM) in terms of texture similarity.%为衡量半色调图像质量或加网算法的优劣,提出平均亮度相似性误差、平均对比度相似性误差和视觉相似性误差以衡量半色调图像与连续调图像之间的视觉相似性.该方法根据人眼视觉局部适应性特征,把图像划分子区域,利用亮度掩蔽和对比度掩蔽的特征,获得各子区域的亮度相似性误差和对比度相似性误差,最终获得平均亮度相似性误差、平均对比度相似性误差和视觉相似性误差分别评价半色调图像与原连续调图像在亮度和纹理的视觉相似性,及半色调图像中的局部缺陷.分别通过与峰值信噪比(PSNR)和权重信噪比(WSNR)在亮度相似性上进行比较,与全局质量因子(UQI)和图像结构相似(SSIM)在评价纹理相似性上进行比较,发现所提质量评价方法比较接近人眼视觉评价的结果.