一项心理学研究：颜色到灰度映射中对比度和亮度的重要性
A Psychological Study: Importance of Contrast and Luminance in Color to Grayscale Mapping
AI总结：
本研究通过心理学实验评估脱色算法，发现基于感知的CIELAB平均表现最佳，而基于空间对比度的算法虽选择时间短但性能较差，强调对比度和亮度在颜色到灰度映射中的重要性。
摘要：
灰度图像在图像处理和计算机视觉任务中至关重要。它们能有效强调亮度和对比度，突出重要的视觉特征，同时易于与其他算法兼容。此外，其简化的表示形式使其在存储和传输方面具有高效性。虽然保持对比度对于维持视觉质量很重要，但其他因素，如保留与特定应用或当前任务相关的信息，可能对实现最佳性能更为关键。为了评估和比较不同的脱色算法，我们设计了一项心理学实验。在实验过程中，参与者被要求在假设的“无色世界”中想象彩色图像，并选择最接近其心理视觉化的灰度图像。我们对两类算法进行了比较：（i）基于感知的简单色彩空间转换算法，和（ii）基于空间对比度的算法，包括基于迭代的方法。我们的实验结果表明，CIELAB在平均表现上更优，进一步证明了基于感知的脱色算法的有效性。另一方面，基于空间对比度的算法表现相对较差，可能是由于直流偏移和人工对比度生成等因素。然而，这些算法表现出更短的选择时间。值得注意的是，没有单一算法在所有测试图像上始终优于其他算法。在本文中，我们将基于我们的实验结果和分析，对颜色到灰度映射中对比度和亮度的重要性进行全面的讨论。
英文摘要：
Grayscale images are essential in image processing and computer vision tasks. They effectively emphasize luminance and contrast, highlighting important visual features, while also being easily compatible with other algorithms. Moreover, their simplified representation makes them efficient for storage and transmission purposes. While preserving contrast is important for maintaining visual quality, other factors such as preserving information relevant to the specific application or task at hand may be more critical for achieving optimal performance. To evaluate and compare different decolorization algorithms, we designed a psychological experiment. During the experiment, participants were instructed to imagine color images in a hypothetical "colorless world" and select the grayscale image that best resembled their mental visualization. We conducted a comparison between two types of algorithms: (i) perceptual-based simple color space conversion algorithms, and (ii) spatial contrast-based algorithms, including iteration-based methods. Our experimental findings indicate that CIELAB exhibited superior performance on average, providing further evidence for the effectiveness of perception-based decolorization algorithms. On the other hand, the spatial contrast-based algorithms showed relatively poorer performance, possibly due to factors such as DC-offset and artificial contrast generation. However, these algorithms demonstrated shorter selection times. Notably, no single algorithm consistently outperformed the others across all test images. In this paper, we will delve into a comprehensive discussion on the significance of contrast and luminance in color-to-grayscale mapping based on our experimental results and analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
