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IVCA: Inter-Relation-Aware Video Complexity Analyzer

Junqi Liao, Yao Li, Zhuoyuan Li, Li Li, Dong Liu

TL;DR

This work addresses the mismatch between video complexity estimates and actual coding bitrate by extending the existing Video Complexity Analyzer (VCA) to account for inter-frame relations. The proposed IVCA introduces three core innovations: feature-domain motion estimation to capture inter-frame motion, a layer-aware weights scheme aligned with hierarchical reference structures, and a reference-based temporal feature that uses potential reference frames rather than only the previous frame. Experimental results on the Inter4K dataset show substantial accuracy gains (3.73% from motion estimation, 2.93% from weighting, and 7.52% total PCC improvement) with only negligible increases in time complexity, demonstrating suitability for real-time video streaming. Collectively, IVCA provides a more nuanced and practically viable tool for dynamic video complexity analysis and codec parameter tuning.

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.

IVCA: Inter-Relation-Aware Video Complexity Analyzer

TL;DR

This work addresses the mismatch between video complexity estimates and actual coding bitrate by extending the existing Video Complexity Analyzer (VCA) to account for inter-frame relations. The proposed IVCA introduces three core innovations: feature-domain motion estimation to capture inter-frame motion, a layer-aware weights scheme aligned with hierarchical reference structures, and a reference-based temporal feature that uses potential reference frames rather than only the previous frame. Experimental results on the Inter4K dataset show substantial accuracy gains (3.73% from motion estimation, 2.93% from weighting, and 7.52% total PCC improvement) with only negligible increases in time complexity, demonstrating suitability for real-time video streaming. Collectively, IVCA provides a more nuanced and practically viable tool for dynamic video complexity analysis and codec parameter tuning.

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.
Paper Structure (8 sections, 10 equations, 5 figures, 1 table)

This paper contains 8 sections, 10 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Methods
  4. Feature-domain motion estimation
  5. Layer-aware weights scheme
  6. Reference-based temporal feature
  7. Experimental Results
  8. Conclusion

Figures (5)

  • Figure 1: The reference structure in x264: 3 Layers, GOP Size 4, Intra Period 250.
  • Figure 2: Illustration of the proposed feature-domain motion estimation in the horizontal direction. Blocks marked with different colors represent feature samples with different energy.
  • Figure 3: Illustration of the distribution of libx264 coding bits and temporal complexity in VCA and IVCA on an inter-coded frame. (a) The heat map of the libx264 coding bits. (b) The heat map of the temporal complexity in VCA. (c) The heat map of the temporal complexity in IVCA.
  • Figure 4: Examples of sequences in test dataset with mild and intense motion.
  • Figure 5: A linear fitting between the IVCA estimated complexity and the actual coding bitrate of libx264.