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Article Type

Review

Abstract

Compressing video is an important and essential function in today's multimedia systems as it enables the efficient storage and transmission of large volumes of video data. The proliferation of high-resolution videos that are being used in various application areas such as video streaming, video conferencing, surveillance, and autonomous systems has caused the strong demand for more efficient compression algorithms. This paper presents an in-depth review of video compression techniques with particular focus on AI methods. It also discusses traditional video coding standards including H. 264/AVC, H. 265/HEVC, and AV1, including motion estimation, transform coding quantization entropy coding, and rate-distortion optimization. The paper provides an extensive review of the latest transformative AI-based techniques which include machine learning, deep learning, and neural networks that are used to effectively enhance compression and visual quality. Lastly, the paper discusses the key challenges like computational complexity, latency, and real-time implementation, and at the same time, it suggests future research paths such as the study of lightweight AI models, hybrid compression systems, and optimization of perceptual quality.

Keywords

Video compression, Artificial Intelligence, Machine learning, Deep learning, Optimization techniques, H.264/AVC, HEVC, AV1, Rate–distortion optimization, Multimedia systems, Neural compression

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