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.

翻译：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较高效率。尽管保留对比度对维持视觉质量很重要，但对于实现最佳性能而言，保留与特定应用或任务相关的信息等其他因素可能更为关键。为评估和比较不同脱色算法，我们设计了一项心理学实验。实验中，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想象的灰度图像。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，以及(ii)基于空间对比度的算法（包括基于迭代的方法）。实验结果表明，CIELAB算法在平均性能上表现更优，进一步验证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，可能归因于直流偏移和人工对比度生成等因素。然而，这些算法的选择时间更短。值得注意的是，没有任何单一算法能在所有测试图像上持续优于其他算法。本文基于实验结果和分析，将深入探讨对比度和亮度在彩色到灰度映射中的重要性。