LaBINet—An Approach for Seamlessly Integrating New Advertisement Into an Existing Scene

Abstract

Billboards in multimedia images are critical for capturing wide audiences through advertising. Currently, no open-source platform exists for automated billboard integration, which impacts industries such as filmmaking, advertising, and sports broadcasting. Effective detection and seamless integration of new advertisements into existing frames are essential for this process. This article introduces LaBINet, a technique that leverages advanced deep learning methodologies to localize existing advertisements and utilizes image registration techniques for seamless integration of new ads. The process begins with generating a probabilistic map using AdSegNet to obtain transformed coordinates. Next, seamless integration is performed using the Poisson equation combined with Laplace matrices. To address the challenge of evaluating image quality in the absence of a reference image, we propose an evaluation method that correlates and statistically verifies subjective and objective scores. Experimental results demonstrate that our method outperforms existing techniques in integrating billboards under various lighting conditions, achieving strong subjective preference scores (76–95%) and low distortion scores (median values ranging from 21.817 to 22.529), indicating superior image quality.