SUSTAINABLE ADVANCEMENTS IN IMAGE AND VIDEO PROCESSING FOR ?MODERN APPLICATIONS
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Abstract
The rapid development of artificial intelligence (AI) has fundamentally changed the field of image and video processing and opened up new possibilities for a variety of applications. This paper looks at the latest strategies shaping the field, focusing on innovation and sustainability. We review cutting-edge technologies, including deep learning frameworks, generative models, and edge AI, and study their impact on real-time processing, resource efficiency, and scalability. For example, the Visual Transformer (ViT) has attracted attention for its superior ability to capture global dependencies in visual data, outperforming traditional convolutional neural networks (CNNs) on various tasks. In addition, we discuss the use of generative adversarial networks (GANs) to improve medical image quality, thereby significantly improving diagnostic accuracy. The paper also addresses pressing sustainability issues by exploring ways to reduce the environmental impact of AI-driven image and video processing. Techniques such as model pruning, quantization, and integration of renewable energy into data centers are explored, and practical solutions are provided to balance performance and energy consumption. This research provides practical insights that can be directly applied to industries such as healthcare, autonomous vehicles, and security systems, and provides a roadmap for adopting energy-efficient and ethical AI practices. Through these analyses, this paper provides valuable insights into current trends, practical applications, and future directions of AI-driven image and video processing. By combining empirical evidence and case studies, we aim to contribute to the ongoing discussion on the role of AI in creating a more sustainable and innovative future.
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