The scene graph is a new data structure describing objects and their pairwise relationship within image scenes. As the size of scene graph in vision applications grows, how to losslessly and efficiently store such data on disks or transmit over the network becomes an inevitable problem. However, the compression of scene graph is seldom studied before because of the complicated data structures and distributions. Existing solutions usually involve general-purpose compressors or graph structure compression methods, which is weak at reducing redundancy for scene graph data. This paper introduces a new lossless compression framework with adaptive predictors for joint compression of objects and relations in scene graph data. The proposed framework consists of a unified prior extractor and specialized element predictors to adapt for 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, a learned distribution model is devised to predict numerical data under complicated conditional constraints. Experiments conducted on labeled or generated scene graphs proves the effectiveness of the proposed framework in scene graph lossless compression task.

翻译：场景图是一种描述图像场景中对象及其成对关系的新型数据结构。随着视觉应用中场景图数据规模的不断增长，如何将这些数据无损且高效地存储到磁盘或通过网络传输成为不可避免的问题。然而，由于场景图复杂的数据结构与分布特性，其压缩研究此前鲜有涉及。现有方案通常采用通用压缩器或图结构压缩方法，难以有效减少场景图数据中的冗余。本文提出了一种新颖的无损压缩框架，通过自适应预测器对场景图数据中的对象与关系进行联合压缩。该框架包含统一先验提取器与专用元素预测器，以适应不同数据元素。此外，为充分利用图元素内部及元素间的上下文信息，本文提出了图上下文卷积（Graph Context Convolution）方法，支持为不同图元素定制差异化的图上下文建模方案。最终，我们设计了一种学习型分布模型，用于在复杂条件约束下预测数值型数据。在标注或生成场景图上开展的实验证明了所提框架在场景图无损压缩任务中的有效性。