Abstract

With the increas of the global aging population, elderly care has become an important social issue around the world. Human-centered telehealth provides more efficient and comfortable health-care services for elderly people through collecting the elderly’s information remotely. Video taken by wearable cameras is one of the most efficient carriers for human-centered telehealth. Whereas wearable cameras are mainly limited in energy supply and computation, the conventional video codecs such as H.26x requiring encoders with powerful processing ability are thus not suitable. Distributed video coding (DVC) based
on the Wyner–Ziv (WZ) coding architecture, namely, WZ video coding, can exploit the source statistics
only at decoders. It thus provides an efficient solution for low-power wearable cameras. Nevertheless, the
compression performance gap between the DVC and the conventional video coding still exists. One of the
main reasons for this weakness is the quality of the side information (SI). As the estimation of the current WZ
frame, the SI provides the important inter-frame correlation for the correlation noise statistics. In this paper, a novel algorithm is proposed for the SI refinement first. The proposed refinement algorithm iteratively
learns the difference between the already decoded information of the current WZ frame and the SI, and
makes a targeted refinement for the SI quality. Subsequently, a progressively refined correlation noise model
is proposed based on the novel SI refinement algorithm. The progressively refined WZ video coding is thus
achieved. The performance evaluations show that the proposed technique advances over the existing DVC
systems. The proposed technique provides an efficient way to improve the video compression performance
for the low-power wearable cameras in human-centered telehealth.