Hybrid Quad-Tree And Nested Multi-Type Tree-Based AI Techniques For Video Compression And Transmission In 5G Applications

Abstract

 Back ground: The emergence of 5G networks has resulted in a revolution in real-time video applications, necessitating effective video compression and transmission solutions that can fulfill high bandwidth, low latency, and improved quality requirements.

Method: The present paper develo ps a hybrid solution as a mesh of Quad-Tree and Nested Multi-Type Tree algorithms and implementation of Artificial Intelligence (AI) to enh a nce the video compression and transmission efficiency under 5G circumstances. The model employs a hierarchical format of quad-trees to spatially compress and multi-type trees to deal with vary video qualities to enhance the most effective resource usage. The dynamic modification of compression parameters is done by machine learning approaches leading to enhancement of rate distortion and reduction of computing overhead. The paper shows that the AI-based algebraic hybrid-tree approaches can be a robust basis of video services of the next generation in 5G.

Results: The proposed solution overcomes the limitations of existing encoding systems such as JPEG, MPEG, AVC, and HEVC by delivering better compression rates while maintaining video quality. Experimental results reveal significant improvements in compression efficiency, transmission reliability, and overall video quality as compared to older methods.

Conclusion: The AI-assisted hybrid-tree-based method would enhance the video compression and transmission of 5G networks to a considerable extent. This performs better than the traditional method where there is optimization in rate-distortion and also on the load. This sorted method provides a favorable platform of next-generation real-time video exploitation.