Motion-adaptive adjacent-reference skipping for distributed video compressive sensing with general decoders

Abstract

On an internet of video things (IoVT), an encoder needs to collect a large number of signal samples to improve the reconstruction quality. It is challenging to some occasions where the resources of an encoder are extremely limited. The distributed video compressive sensing (DVCS) can save a lot of resources for the encoder. For the skip-block coding at such an encoder, this paper proposes a motion-adaptive adjacent-reference skipping (MAS) algorithm for DVCS with general decoders. The proposed algorithm makes full use of the spatial-temporal correlation between consecutive frames, and the reconstruction quality can be improved significantly. What’s more, the skipping ratio of non-keyframes is adaptive to the difference of their motion-speeds. The proposed algorithm does not need to change any decoder, so it can be easily applied to general decoders. The simulation results show that under different skipping ratios, the proposed algorithm can achieve better reconstruction quality than other existing algorithms, and thus improve the energy-efficiency of the encoder.