Image Binarization Using Floyd-Steinberg Dithering Algorithm in Error Diffusion

When applying the Floyd-Steinberg dithering algorithm for image binarization via error diffusion, the conversion of grayscale images to binary black-and-white format enables clearer distinction of image content. This processing method supports frequency-modulated screening output by implementing a sequential pixel scan where quantization errors are distributed to neighboring pixels using fixed ratios (7/16 to right, 3/16 below-left, 5/16 below, 1/16 below-right). The algorithm enhances output clarity through error propagation that maintains local intensity averages, making images more legible and identifiable. Additionally, this technique finds applications in other image processing domains such as edge detection and pattern recognition, where threshold-based operations benefit from the algorithm's error-diffusion approach. Consequently, for scenarios requiring robust image processing, the Floyd-Steinberg dithering algorithm serves as a valuable tool for binarization tasks, particularly when integrated with raster-based processing pipelines that handle pixel-level error correction.