红色和色素沉着在图像中的主要特征在于它们在色度图上的特定位置。与代表照明和皮肤问题严重程度的强度不同，像素与特征点的接近程度决定了它可能被呈现为红色和色素沉着的可能性。

为了解决这个问题，Lab颜色空间证明了其优势。它旨在模拟人类视觉系统的颜色感知和解释方法。因此，在Lab颜色空间中，任何两种颜色之间的欧式距离具有实际的感知意义。

虽然Lab颜色空间可能不像RGB和HSV那样直观或易于理解，但在计算机视觉领域广泛应用。它的应用克服了不同照明条件带来的挑战，因为颜色之间的距离具有感知意义。此外，它还是一种强大的颜色图像描述符。

通过将图像转换为Lab颜色空间，并利用a-b通道平面上的距离，可以量化像素与皮肤问题的关联性。然而，需要注意的是，Lab颜色空间中的某些颜色在RGB颜色空间中无法准确表示。因此，在使用OpenCV保存图像时，结果图像可能会出现一些轻微失真。尽管如此，这种失真是很小的，不会对整体结果产生显著影响。

The primary characteristic of redness and hyperpigmentation in an image lies in their specific location on the chromaticity diagram. Unlike intensity, which primarily represents illumination and the severity of the skin problem, the proximity of a pixel to the characteristic point determines its likelihood of being rendered as redness and hyperpigmentation. To address this, the Lab color space proves to be advantageous. It aims to emulate the human visual system’s color perception and interpretation methodology. Consequently, the Euclidean distance between any two colors in the Lab color space carries actual perceptual significance. While the Lab color space may not be as intuitive or easily comprehensible as RGB and HSV, it is applied extensively in the area of computer vision. Its utilization overcomes challenges posed by varying lighting conditions, as the distance between colors holds perceptual meaning. Furthermore, it serves as a robust color image descriptor. By converting the image into the Lab color space and leveraging the distance on the a-b channel plane, a pixel’s association with the skin problem can be quantified. However, it is important to note that certain colors within the Lab color space cannot be accurately represented in the RGB color space. As a result, when saving the images with OpenCV, some minor distortion may occur in the result images. Nonetheless, this distortion is minimal and does not significantly impact the overall outcome.