Evaluating pixel pair for image matting using hybrid feature distortion
Zhanan Lin, Yuanlie He, Wensheng Li, Qiping Huang, Zhilin Deng, Xiantao Su, Yihui Liang · 2025
Image matting is a technique dedicated to the precise extraction of foreground opacity from a target image, widely used in image compositing and video editing. Pixel pair optimization-based methods select the best pair of foreground and background pixels for each unknown pixel based on a pixel pair evaluation function, thereby achieving opacity estimation. However, existing pixel pair evaluation function methods are inaccurate, causing the alpha values of some foreground pixels to be erroneously estimated a s 0. To address this issue, this paper proposes a Hybrid Feature Distortion pixel pair Evaluation function (HFDE). This method designs a Semantic Distance Feature vector Distortion (SDFD) term from the dimension of high-order features and combines it with existing low-order feature evaluation terms to measure the quality of pixel pairs from both dimensions. Experimental results show that, compared to existing pixel pair evaluation functions, the hybrid feature distortion pixel pair evaluation function can more accurately measure the quality of pixel pairs improving the image matting performance.