  To exploit high temporal correlations in video frames of the same scene, the
current frame is predicted from the already-encoded reference frames using
block-based motion estimation and compensation techniques. While this approach
can efficiently exploit the translation motion of the moving objects, it is
susceptible to other types of affine motion and object occlusion/deocclusion.
Recently, deep learning has been used to model the high-level structure of
human pose in specific actions from short videos and then generate virtual
frames in future time by predicting the pose using a generative adversarial
network (GAN). Therefore, modelling the high-level structure of human pose is
able to exploit semantic correlation by predicting human actions and
determining its trajectory. Video surveillance applications will benefit as
stored big surveillance data can be compressed by estimating human pose
trajectories and generating future frames through semantic correlation. This
paper explores a new way of video coding by modelling human pose from the
already-encoded frames and using the generated frame at the current time as an
additional forward-referencing frame. It is expected that the proposed approach
can overcome the limitations of the traditional backward-referencing frames by
predicting the blocks containing the moving objects with lower residuals.
Experimental results show that the proposed approach can achieve on average up
to 2.83 dB PSNR gain and 25.93\% bitrate savings for high motion video
sequences
