In this paper, we identify a critical limitation in the MPEG-I Scene Description (SD) format: the absence of a native mechanism for representing semantic relationships between objects. To address this, we propose a relationship extension framework that enables both static and dynamic inter-object semantics within 3D scenes. The proposed solution includes two integration strategies: (1) embedding relationship information within existing extensions such as scene interactivity and scene dynamic, and (2) introducing external relationship extensions that preserve the integrity of the core SD specification. We provide a concrete syntax and demonstrate the utility of our approach through applications in scene reconstruction and ISOBMFF-based integration. The results show that both options enable context-aware manipulation and efficient restoration of inter-object relations, supporting advanced interactive and media synchronization scenarios. We propose further evaluation and adoption of these extensions to enhance interoperability and backward compatibility in future MPEG standards.

3D scene graph generation (3DSGG), which involves classifying objects and predicates, is an emerging topic in 3D scene understanding. Recent studies leveraging graph neural networks (GNNs) have introduced sophisticated architectures that enhance classification performance. However, since GNNs serve as the core and constitute the majority of parameters in 3DSGG models, their computational demands substantially increase overall complexity, which makes it difficult to determine the optimal model capacity. In this paper, we propose the first compression framework for lightweight 3DSGG models, based on pruning-as-search and knowledge distillation. This framework integrates multiple strategies and modules. In phase 1, the framework identifies the optimal compression ratio through pruning-as-search. In phase 2, to mitigate the accuracy loss incurred during compression, we employ structured pruning and a novel knowledge distillation strategy that effectively transfers precise information from the teacher to the compressed model. Experimental results show that our approach reduces model size by more than half while improving classification accuracy. Code is available athttps://github.com/hojunking/3DSGG-compression.

Abstract The authors propose a compression strategy for a 3D human pose estimation model based on a transformer which yields high accuracy but increases the model size. This approach involves a pruning‐guided determination of the search range to achieve lightweight pose estimation under limited training time and to identify the optimal model size. In addition, the authors propose a transformer‐based feature distillation (TFD) method, which efficiently exploits the pose estimation model in terms of both model size and accuracy by leveraging transformer architecture characteristics. Pruning‐guided TFD is the first approach for 3D human pose estimation that employs transformer architecture. The proposed approach was tested on various extensive data sets, and the results show that it can reduce the model size by 30% compared to the state‐of‐the‐art while ensuring high accuracy.

In this paper, we propose a novel in-loop filter design for video compression. Our approach aims to replace existing deblocking filter and SAO (Sample Adaptive Offset) of HEVC standard with multi-modal/multi-scale convolutional neural network (MMS-net). The proposed CNN architecture consists of two sub-networks of different scales. An input image is down-sampled first and restored through the lower scale network, then the output image from it is fed into higher scale network concatenated with the original input image. Moreover, to boost the restoration performance, the proposed architecture utilizes information resides in the coded sequence. Specifically, the compression parameters from coding tree units (CTU) are exploited as input to CNN, which helps to alleviate blocking artifacts on the reconstructed images. In the experiments, our method reduces the average BD-rate by 4.55% and 8.5%, respectively, compared with the conventional neural network based approach [1] and HEVC reference software HM16.7 [2] in `All Intra - Main' configuration.

The conventional embedded wavelet image coder exploiting the adjacent neighbors is limited to efficiently compress the sign coefficients, since the wavelet coefficients are highly correlated along the dominant image features, such as edges or contours. To solve the problem, in this paper, we propose the direction-adaptive sign context modeling by adaptively exploiting the neighbors suitable to the dominant image features. Experimental results show that the sign coding based on the proposed context modeling reduces the sign bits up to 5.5% compared to the conventional sign coding method.