This standard defines a set of compression, decompression, and packaging tools and a mechanism for coding intermediate features of deep neural networks, including deep convolutional networks, transformers, and diffusion models, that are widely adopted in intelligent processing and analytics applications. Examples of such applications are intelligent transportation and smart cities.
- Standard Committee
- C/DC - Data Compression Standards Committee
- Status
- Active PAR
- PAR Approval
- 2024-05-22
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/DC - Data Compression Standards Committee
- Working Group
-
MFC - Multi-Modality Feature Coding Working Group
- IEEE Program Manager
- Meng Zhao
Contact Meng Zhao - Working Group Chair
- LINGYU DUAN
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
P3483.1
Standard for Structured Point Sequence Compression
This standard defines a set of compression and decompression tools for Structured Point Sequences that exhibit highly correlated spatial structures and strong temporal relationships among points with collectively inherent semantic meanings. The compression tools reduce data size by 60% or more while enabling a low computational complexity of 30 frames per second on average. The standard supports Structured Point Sequences with diverse dimensions and relationships.
P3483.2
Standard for Semantic Segmentation Map Coding
This standard defines a set of compression and decompression tools for the semantic segmentation map. These tools are widely adopted in intelligent analytics and Artificial Intelligence Generated Content (AIGC) applications. On the Cityscapes dataset widely used in urban scene understanding, the tools achieve up to 800 times of compression encoding each pixel with less than 0.01 bits.
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