SIFT-Based Homographies for Efficient Multiview Distributed Visual Sensing

Considering the scarce resources in visual sensor networks, video coding solutions are necessary to address these constraints, namely the limited energy, memory, and bandwidth. The distributed video coding (DVC) paradigm is able to address these limitations by shifting most of the complexity to the decoder, typically a central location with a significant amount of resources. In a DVC context, the video codec rate-distortion (RD) performance is strongly dependent on the efficient generation of the so-called side information (SI). While in monoview DVC solutions the SI is generated by exploiting only the temporal correlation between frames, in multiview DVC (MV-DVC) solutions, the SI may be estimated by exploiting both the temporal and interview correlations. In this paper, a novel MV-DVC solution is proposed to improve the state-of-the-art MV-DVC RD performance with two key contributions in terms of an interview SI creation and SI fusion. According to the obtained experimental results, the proposed solution improves the RD performance associated to the current MV-DVC state-of-the-art solutions, exhibiting bitrate savings $sim 10$ % for the same output video quality, without increasing the encoding complexity and eventually also the decoding complexity.