Abstract

The rise of holographic video processing has transformed multimedia experiences by providing highly immersive and realistic visuals. However, efficiently processing these high-dimensional holographic datasets poses significant computational challenges. Current methods often struggle with latency, scalability, and maintaining quality during real-time rendering. Addressing these limitations requires the integration of advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. This research introduces a novel approach leveraging an adaptive Support Vector Machine (adaSVM) algorithm for holographic video processing, integrated with multimedia data fusion. The adaSVM dynamically adjusts its parameters based on input data complexity, ensuring robust classification and processing of holographic frames. The proposed method incorporates intelligent feature extraction, dimensionality reduction, and predictive modeling to optimize resource utilization while maintaining visual quality. Experimental evaluation using a dataset of 500 holographic video sequences shown superior performance. The adaSVM achieved an accuracy of 96.8%, a processing speed improvement of 34.2%, and a reduction in latency by 28.7% compared to traditional SVM and Convolutional Neural Network-based approaches. Additionally, the method shown enhanced scalability in handling large datasets, with consistent performance across varying resolutions and frame rates. The results underscore the potential of adaSVM in revolutionizing holographic video processing for applications in entertainment, education, and medical imaging. This integration of AI and ML represents a significant step toward efficient and scalable solutions for next-generation multimedia systems.