Data Representation in Hybrid Coding Framework for Feature Maps Compression

Recently, a new paradigm of transmitting and compressing intermediate deep learning features (i.e., feature maps) for distributed visual analysis systems is emerging. As the fundamental infrastructure in such paradigm, research and standardization for feature maps coding has attracted more and more attention. In this paper, to improve the state-of-the-art hybrid coding framework which integrates the traditional video codecs to compress feature maps, we investigate the data representation procedure in such coding framework. Specifically, we proposed three modes in Repack module to help explore inter-channel redundancy, and we explore the fidelity maintenance ability of two modes in Pre-Quantization modules. It is worth mentioning that the proposed coding modes have been partially adopted in to the ongoing AVS (Audio Video Coding Standard Workgroup) - Visual Feature Coding Standard.