Micro-LED displays achieve image rendering beyond the physical resolution of the display by means of sub-pixel multiplexing technology, which effectively reduces the production cost while improving the perceived resolution. However, the traditional unweighted average rendering algorithm for the triangular arrangement of RGB subpixels easily leads to the problem of color distortion, which in turn seriously affects the image display quality and user viewing experience. To solve this problem, this paper proposes an adaptive weighted subpixel rendering algorithm based on color difference. The algorithm consists of an image preprocessing module, a color space conversion module, an adaptive weight calculation module, and a subpixel rendering module. Firstly, the image preprocessing module extends the boundaries of the input image to ensure that no boundary-crossing problem occurs in the subsequent subpixel rendering process. Next, the color space conversion module separates and expands the channels of the read pixels and converts them from the sRGB color space to the CIELab color space. Subsequently, the adaptive weight calculation module achieves scene matching by calculating the color difference polarity between pixels and assigns values to the output subpixels. If it fails to match the corresponding scene, the center pixel is selected and the weight value is calculated based on the color difference of the neighboring pixels. Finally, the subpixel rendering module outputs the grayscale value of the subpixel based on the calculated weight value to complete the rendering of the final image. The experimental results show that compared with the traditional unweighted average subpixel rendering algorithm, the color difference-based adaptive weighted subpixel rendering algorithm proposed in this paper performs better on the DIV2K dataset. Specifically, the mean square error is reduced by 14.537 61, the peak signal-to-noise ratio is improved by 0.828 21 dB, and the structural similarity is improved by 0.014 96, the feature similarity index increased by 0.000 83, and the fidelity of visual information increased by 0.009 66. The algorithm significantly suppresses the color distortion problem, which effectively improves the display quality and the user viewing experience.