Enhancing Real-Time Video Processing With Artificial Intelligence: Overcoming Resolution Loss, Motion Artifacts, And Temporal Inconsistencies

Purpose: Traditional video processing techniques often struggle with critical challenges such as low resolution, motion artifacts, and temporal inconsistencies, especially in real-time and dynamic environments. Conventional interpolation methods for upscaling suffer from blurring and loss of detail, while motion estimation techniques frequently introduce ghosting and tearing artifacts in fast-moving scenes. Furthermore, many traditional video processing algorithms process frames independently, resulting in temporal instability, which causes flickering effects and unnatural motion transitions. These limitations create significant barriers in applications that require high-quality, real-time video processing, such as surveillance, live streaming, autonomous navigation, and medical imaging. This study aims to address these challenges by exploring AI-driven video enhancement techniques, leveraging deep learning-based super-resolution models, optical flow estimation, and recurrent neural networks (RNNs) to improve video quality. By integrating Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), and Transformer-based architectures, we propose a framework that reconstructs lost details, enhances motion smoothness, and maintains temporal consistency across frames. The primary goal is to demonstrate how AI-powered solutions can outperform traditional video processing methods, enabling sharper, artifact-free, and temporally stable video quality. This research contributes to the growing field of AI-enhanced video processing and highlights its potential to revolutionize real-time applications across various industries. Design/Methodology/Approach: To develop a robust AI-driven video enhancement framework, this study employs a multi-stage deep learning approach integrating Super-Resolution, Optical Flow, and Temporal Consistency models. The methodology consists of the following key components: Super-Resolution for Detail Restoration We implemented ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) to upscale low-resolution video frames while preserving fine details. The model is trained on high-quality datasets, ensuring improved video clarity and structure preservation. Deep Learning-Based Optical Flow for Motion Estimation Traditional motion estimation techniques, such as Lucas-Kanade or Farneback Optical Flow, are replaced with deep learning models like RAFT (Recurrent All-Pairs Field Transforms) and Flownet2. These models provide precise motion tracking and artifact reduction in dynamic scenes. Temporal Consistency Using Recurrent Neural Networks (RNNs) and Transformers To address frame flickering and temporal instability, we use Long Short-Term Memory (LSTM) networks and Temporal Transformer models. These models ensure smooth transitions between frames, preventing abrupt visual inconsistencies. Implementation and Training Process The proposed models are trained and tested on benchmark video datasets, including YouTube-VOS and DAVIS. Evaluation metrics such as PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), and LPIPS (Learned Perceptual Image Patch Similarity) are used to measure improvements in video quality, motion accuracy, and temporal consistency. Findings/Results: Our experimental evaluations demonstrate that AI-powered video enhancement methods significantly outperform traditional techniques across multiple quality metrics. Key findings include: Higher Resolution and Detail Preservation The ESRGAN-based Super-Resolution model achieves higher PSNR and SSIM scores, ensuring sharper image reconstruction without excessive blurring or artifacts. Compared to bicubic interpolation and conventional upscaling, our model preserves fine textures and edges more effectively. Reduction of Motion Artifacts Optical flow estimation with RAFT and Flownet2 results in a 60% reduction in motion artifacts compared to traditional Lucas-Kanade methods. Fast-moving scenes, which often suffer from ghosting and tearing, show notable improvements in object continuity and motion clarity. Temporal Consistency Improvements The LSTM-based Temporal Consistency model eliminates frame flickering and inconsistencies, achieving a 35% improvement in temporal coherence. Transformer-based solutions provide smoother transitions between frames, making the video appear more natural and visually stable. Real-Time Feasibility Optimized models using TensorRT and ONNX runtime demonstrate near real-time processing speeds, making AI-based solutions viable for live applications in surveillance, broadcasting, and autonomous systems. Originality/Value: This research presents a novel integration of AI-based Super-Resolution, Optical Flow, and Temporal Consistency models to enhance real-time video processing. While prior studies have explored individual deep learning approaches for video enhancement, our framework combines multiple AI-driven techniques to address resolution loss, motion artifacts, and temporal inconsistencies comprehensively. The originality of this study lies in: Combining Super-Resolution, Optical Flow, and RNN-based Temporal Stability in a unified AI-driven pipeline. Demonstrating real-time feasibility of deep learning models through hardware acceleration and optimization techniques. Evaluating AI-based video enhancement across diverse datasets to ensure applicability across surveillance, gaming, medical imaging, and streaming. By offering a scalable, high-performance AI-driven solution, this study contributes to the advancement of real-time video processing, making it an essential reference for researchers, engineers, and industries working on AI-powered multimedia applications. Paper Type: Applied AI Research and Experimental Study.