Authors: Bolin Chen, Shanzhi Yin, Peilin Chen, Shiqi Wang, Yan Ye

Published on: February 03, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.0214

Summary

• What is new: The paper covers new grounds in the use of artificial intelligence for content creation, focusing on how it can enhance digital content acquisition and visual compression.
• Why this is important: Traditional methods of digital content compression face limitations in performance gains and functionalities.
• What the research proposes: Utilizing artificial intelligence, specifically deep generative models, for visual data compression to achieve ultra-low bitrate communication and high-fidelity content reconstruction.
• Results: The review shows significant potential in AI-generated content for improving visual compression techniques, especially in ultra-low bitrate scenarios and intelligent machine analysis.

Technical Details

Technological frameworks used: Deep generative models

Models used: AI techniques for visual compression

Data used: Visual data for compression and reconstruction

Potential Impact

Creative industries, digital content providers, streaming platforms, telecom companies, and tech firms focusing on data compression and transmission technologies could be disrupted or benefit.

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