Scene Graph Lossless Compression with Adaptive Prediction for Objects and Relations

Weiyao Lin, Yufeng Zhang, Wenrui Dai, Huabin Liu, John See, Hongkai Xiong

Research output: Contribution to journalArticlepeer-review

Abstract

The scene graph is a novel data structure describing objects and their pairwise relationship within image scenes. As the size of scene graphs in vision and multimedia applications increases, the need for lossless storage and transmission of such data becomes more critical. However, the compression of scene graphs is less studied because of the complicated data structures involved and complex distributions. Existing solutions usually involve general-purpose compressors or graph structure compression methods, which are weak at reducing the redundancy in scene graph data. This article introduces a novel lossless compression framework with adaptive predictors for the joint compression of objects and relations in scene graph data. The proposed framework comprises a unified prior extractor and specialized element predictors to adapt to different data elements. Furthermore, to exploit the context information within and between graph elements, Graph Context Convolution is proposed to support different graph context modeling schemes for different graph elements. Finally, an overarching framework incorporates the learned distribution model to predict numerical data under complicated conditional constraints. Experiments conducted on labeled or generated scene graphs demonstrate the effectiveness of the proposed framework for scene graph lossless compression.
Original languageEnglish
Article number196
JournalACM Transactions on Multimedia Computing, Communications and Applications
Volume20
Issue number7
Early online date27 Mar 2024
DOIs
Publication statusE-pub ahead of print - 27 Mar 2024

Keywords

  • Scene graph compression
  • image compression
  • lossless compression

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications

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