Semantic segmentation and description for video transcoding
We present an automatic content-based video transcoding algorithm, which is based on how humans perceive visual information. The transcoder support multiple video objects and their description. First the video is decomposed into meaningful objects through semantic segmentation. Then the transcoder adapts its behavior to code relevant (foreground) and non-relevant objects differently. Both objects-based and frame-based encoders are combined with semantic segmentation. Experimental results show that the use of semantics and description prior to transcoding reduces the bandwidth requirements and makes it possible to adapt the video representation to limited network and terminal device capabilities still retaining the essential information.