How VidCom² Transforms Video Compression for Efficient AI Processing

Breaking Through Video Understanding Efficiency: How VidCom² Optimizes Large Language Model Performance Introduction: The Efficiency Challenges of Video Large Language Models As artificial intelligence advances to understand continuous video content, Video Large Language Models (VideoLLMs) have become an industry focal point. These models must process massive visual data – a typical video contains 32-64 frames, each decomposed into hundreds of visual tokens. This data scale creates two core challenges: High Computational Resource Consumption: Processing 32-frame videos requires ~2,000 visual tokens, causing response latency up to 618 seconds Critical Information Loss Risks: Uniform compression might delete unique frames like skipping crucial …