g., MPEG-4 AVC/H.264, High Efficiency Video Coding (HEVC) and the recently-proposed versatile video coding (VVC), have achieved great success. Particularly, the mainstream traditional video coding frameworks, e. In specific, the traditional hybrid video coding (HVC) frameworks have evolved over the decades by gradually integrating the efficient transformation, quantization, and entropy coding into the compression procedure, achieving a trade-off on the rate–distortion optimization. Once the raw video content information needs to be displayed for human eyes or employed for multimedia analysis applications, a reverse decoding operation will be applied to recover such compact representations to the raw pixel deployment. e., bitstream), during the transmission procedure, ,. To alleviate transmission burden and save storage resources, the video content is typically compressed into a compact representation ( i. Extensive experiments on various vision tasks demonstrate that the proposed SSVC framework could directly support multiple intelligent tasks just depending on a partially decoded bitstream, saving bitrate consumption for intelligent analytics.Īs a pivotal role in the modern multimedia industry, video occupies most of the communication bandwidth. Specifically, we introduce optical flow to encode continuous motion information and reduce cross-frame redundancy via a predictive coding architecture, then the optical flow and residual information are reorganized into SSB, which enables the proposed SSVC could better adaptively support video-based downstream intelligent applications. Thus, we present a reformulation of semantically structured bitstream (SSB) in SSVC which contains both static object characteristics and dynamic motion clues. Video signals contain more rich dynamic motion information and redundancy. In this paper, we propose an advanced Semantically Structured Video Coding (SSVC). However, the SSIC considers image coding and its generated SSB only contains the static object information.
Semantically Structured Image Coding (SSIC) (Sun et al., 2020) makes the first attempt to enable partial-decoding image intelligent task analysis via a Semantically Structured Bitstream (SSB).
Traditional media coding schemes typically encode image or video into a semantic-unknown binary stream, which fails to directly support downstream intelligent tasks at the bitstream level.
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