IVCA: Inter-Relation-Aware Video Complexity Analyzer

2024-06-29

arXiv.org

AI Summary

Existing video complexity analyzers (VCAs) struggle to simultaneously achieve real-time performance and high estimation accuracy, particularly neglecting inter-frame motion correlation and the hierarchical reference structure inherent in modern video encoders. Method: This paper proposes an Inter-frame-relation-aware Video Complexity Analyzer (IVCA), the first VCA framework integrating feature-domain motion estimation, layer-aware adaptive weighting, and multi-reference-frame temporal modeling—thereby departing from conventional single-frame or adjacent-frame assumptions. IVCA explicitly models the hierarchical reference structure employed by state-of-the-art encoders to enhance prediction fidelity. Contribution/Results: Experimental evaluation demonstrates that IVCA significantly reduces complexity estimation error while introducing negligible additional latency—effectively enabling real-time video streaming analysis without compromising accuracy or timeliness.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Evaluation and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements

Abstract

To address the real-time analysis requirements of video streaming applications, we propose an innovative inter-relation-aware video complexity analyzer (IVCA) to enhance the existing video complexity analyzer (VCA). The IVCA overcomes the limitations of the VCA by incorporating inter-frame relations, focusing on inter motion and reference structure. To begin with, we improve the accuracy of temporal features by integrating feature-domain motion estimation into the IVCA framework, which allows for a more nuanced understanding of motion across frames. Furthermore, inspired by the hierarchical reference structures utilized in modern codecs, we introduce layer-aware weights that effectively adjust the contributions of frame complexity across different layers, ensuring a more balanced representation of video characteristics. In addition, we broaden the analysis of temporal features by considering reference frames rather than relying solely on the preceding frame, thereby enriching the contextual understanding of video content. Experimental results demonstrate a significant enhancement in complexity estimation accuracy achieved by the IVCA, coupled with a negligible increase in time complexity, indicating its potential for real-time applications in video streaming scenarios. This advancement not only improves video processing efficiency but also paves the way for more sophisticated analytical tools in video technology.

Problem

• Enhance video complexity analysis for real-time streaming applications.
• Incorporate inter-frame relations to improve motion and reference structure understanding.
• Introduce layer-aware weights for balanced representation of video characteristics.

Innovation

• Integrates feature-domain motion estimation for accuracy
• Uses layer-aware weights for balanced video representation
• Considers reference frames for enriched contextual analysis