  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.
