Collaborative Scalable Visual Compression for Human-Centered Videos

Machine intelligence systems have been increasingly widely deployed in real-world circumstances, while the conventional human-vision oriented video coding schemes are inefficient to be embedded in large-scale systems and further support a wide range of applications. There have been urgent demands for a new generation of compression framework to efficiently encodes visual data, where the compression and analytics for machine vision and human perception can be jointly optimized. To this end, we propose a novel visual compression framework to provide visual contents with different granularity for both human and machine vision tasks collaboratively. The proposed scalable compression framework maintains the critical semantic information in a basic layer, so that it is capable of supporting the accurate machine vision analysis under a tight bit-rate constraint. It is scalable to provide visual representations of different granularity to support various kinds of tasks, including video reconstruction that serves human vision examination. Experimental results on the human-centered videos have demonstrated the promising functionality of scalable visual coding with improved efficiency for high-performance machine analysis and human perception.