Abstract
The evolution of video technologies has led to an exponential growth in multimedia data, demanding advanced techniques for efficient video coding and compression. Multi-dimensional signal processing plays a critical role in meeting these demands by enabling efficient representation, transformation, and reduction of spatiotemporal redundancies. This paper explores core multi-dimensional signal processing frameworks—such as 2D and 3D transforms, motion estimation, and rate-distortion optimization—used in modern video codecs like HEVC, VVC, and AV1. Additionally, emerging AI-enhanced hybrid models are discussed to demonstrate how multi-dimensional techniques support adaptive and high-efficiency coding. The paper provides a comparative analysis of these methods in terms of performance, complexity, and compression gains, alongside future directions in immersive video and real-time streaming.
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