In modern multimedia systems, efficient video processing is critical, especially in resource-constrained environments such as IoT-based camera networks, autonomous platforms, and wireless sensor multimedia systems. A key bottleneck in video compression and understanding is block motion estimation (ME), a process that remains computationally expensive despite the development of fast search techniques. This work introduces an Optimal Stopping Theory (OST) algorithm for block motion estimation based on the assessment of spatiotemporal differences within and across video frames. It also proposes a semantic-aware motion estimation framework that integrates Foundation Models (FMs) with the OST-based decision process. By leveraging pretrained visual models such as Vision Transformers (ViT) and the Segment Anything Model (SAM), the framework extracts semantic attention scores that indicate the importance of motion within specific spatial regions. These scores are fused with traditional distortion-based metrics, such as the Sum of Absolute Differences (SAD), to guide a hybrid stopping criterion that jointly considers motion magnitude and semantic relevance. The resulting adaptive algorithm stops early in redundant regions while continuing the search in areas where motion is semantically significant. Experiments compare the proposed solution with widely used approaches from the literature on benchmark and multimodal video datasets. The proposed method achieves a significant reduction in computation with minimal accuracy loss and improved semantic coverage. The results highlight the benefits of bridging low-level motion analysis with high-level semantic reasoning, offering a promising direction for efficient multimodal video understanding in next-generation smart systems.

翻译：在现代多媒体系统中，高效视频处理至关重要，尤其是在资源受限环境（如基于物联网的摄像头网络、自主平台及无线传感器多媒体系统）中。视频压缩与理解的关键瓶颈在于块运动估计，即使发展出快速搜索技术，该过程依然计算开销巨大。本文提出一种基于视频帧内及帧间时空差异评估的最优停止理论算法用于块运动估计，并进一步构建了将基础模型与最优停止理论决策过程相结合的语义感知运动估计框架。通过利用预训练视觉模型（如视觉Transformer和分割一切模型），该框架提取表征特定空间区域运动重要性的语义注意力分数。这些分数与基于失真的传统度量（如绝对差值和）相融合，形成同时考虑运动幅度与语义关联度的混合停止准则。由此产生的自适应算法在冗余区域提前停止搜索，同时在语义重要的运动区域持续搜索。实验将所提方案与文献中广泛使用的方法在基准数据集及多模态视频数据集上进行对比，结果表明该方法在保持极小精度损失和提升语义覆盖的前提下显著降低了计算量。该成果凸显了连接低层次运动分析与高层次语义推理的优势，为下一代智能系统中的高效多模态视频理解提供了有前景的研究方向。