<![CDATA[Video quality degradation due to low resolution, noise, and limited frame rate poses a significant challenge in modern digital content ecosystems, ranging from streaming services to real-time surveillance systems. This study presents a systematic comparative analysis of three categories of artificial intelligence-based video optimization techniques super-resolution, denoising, and frame interpolation benchmarked against conventional methods through simulation on five representative video test clips. Six state-of-the-art deep learning models were evaluated using four quantitative metrics: PSNR, SSIM, LPIPS, and VMAF. Results demonstrate that AI-based methods consistently outperform conventional approaches, with VRT achieving optimal performance in super-resolution (PSNR 34.2 dB) and RIFE attaining the best frame interpolation score (SSIM 0.941).]]>
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