Advanced Neural Frame Generation and Super-Resolution: A Comprehensive Study of AI-Driven Video Enhancement Technologies
Jwalin Thaker - · International Journal on Science and Technology · 2023
This paper addresses the critical challenges in AI-driven video enhancement, specifically the computational complexity and visual artifacts associated with neural frame generation and super-resolution techniques. We propose a novel hybrid architecture that integrates Deep Learning Super Sampling (DLSS) with advanced neural frame interpolation methods to over- come these limitations. Our theoretical framework introduces a unified approach for simultaneous frame generation and resolution enhancement, with potential for significant improvements in video quality. The proposed architecture features three key innovations: (1) a multi-scale feature extraction pipeline that preserves temporal consistency across generated frames, (2) an adaptive sampling mechanism that theoretically optimizes computational resource allocation based on scene complexity, and (3) a perceptual loss function specifically designed for temporal coherence in upscaled video content. We analyze the theoretical advantages of this approach for high-motion scenarios and low-resolution source materials, demonstrating how the architecture could address current limitations in video enhancement technologies through its innovative design principles rather than through extensive experimental validation.