In traditional stroke-based image-to-painting techniques, strokes are often repeated in already drawn areas, resulting in ineffective rendering, low utilization of strokes, and low image quality. This paper presents an approach for image-to-painting that dynamically assigns strokes based on the intrinsic characteristics of different regions in the target image. The proposed method uses an estimation technique to determine the amount of information in different image regions to generate a sequence of content richness. The number of strokes is then dynamically assigned based on the sequence of content richness, which results in a sequence of strokes. Afterward, the distribution of strokes on the canvas is flexibly adjusted based on their sequence, allowing for the accurate rendering of the target image. In addition, the original network model is optimized and improved to make the stroke rendering more accurate and thus obtain a final result that better fits the input image. The experimental results show that the proposed approach can scientifically allocate strokes based on the inherent differences in different image regions, which leads to effective rendering, improved utilization of strokes, and higher visual performance. Furthermore, many indicators, such as the peak signal-to-noise ratio, structural similarity, and Learned Perceptual Image Patch Similarity demonstrate higher image quality compared with those obtained by traditional technologies.