<![CDATA[Video has become a fundamental component in various aspects of modern life. People widely use this medium for a range of purposes, from consuming entertainment content to engaging in online learning activities. However, technical issues related to network infrastructure remain a major challenge. Problems such as high latency, bandwidth fluctuations, and unstable connections often lead to a degraded user experience—ranging from disruptive buffering to sudden drops in video resolution. To address these challenges, researchers have begun developing AI-based approaches for optimizing video compression. Two widely used deep learning architectures are Convolutional Neural Networks (CNNs), which are effective for visual feature extraction, and Generative Adversarial Networks (GANs), which can reconstruct data with high precision. The combination of these techniques enables a significant reduction in video file size without compromising visual quality. Moreover, these systems are designed with adaptive mechanisms that dynamically adjust encoding parameters based on the user’s network conditions. Such implementations allow for more stable video delivery even under limited bandwidth conditions.]]>
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