International Journal on Science and Technology

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Advanced Neural Frame Generation and Super-Resolution: A Comprehensive Study of AI-Driven Video Enhancement Technologies

Author(s) Mr. Jwalin Thaker
Country United States
Abstract 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.
Keywords Visual Computing, Video Enhancement, Neural Frame Generation, Super-Resolution, AI, Deep Learning
Field Engineering
Published In Volume 14, Issue 1, January-March 2023
Published On 2023-01-06
DOI https://doi.org/10.71097/IJSAT.v14.i1.6694
Short DOI https://doi.org/g9rnt5
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