In this paper, we analyze focus mismatches among cameras utilized in a multiview system, and propose techniques
to efficiently apply our previously proposed adaptive reference filtering (ARF) scheme to inter-view prediction in
multiview video coding (MVC). We show that, with heterogeneous focus setting, the differences exhibit in images
captured by different cameras can be represented in terms of the focus setting mismatches (view-dependency) and
the depths of objects (depth-dependency). We then analyze the performance of the previously proposed ARF
in MVC inter-view prediction. The gains in coding efficiency show a strong view-wise variation. Furthermore,
the estimated filter coeffcients demonstrate strong correlation when the depths of objects in the scene remain
similar. By exploiting the properties derived from the theoretical and performance analysis, we propose two
techniques to achieve effcient ARF coding scheme: i) view-wise ARF adaptation based on RD-cost prediction,
which determines whether ARF is beneficial for a given view, and ii) filter updating based on depth-composition
change, in which the same set of filters will be used (i.e., no new filters will be designed) until there is significant
change in the depth-composition within the scene. Simulation results show that significant complexity savings
are possible (e.g., the complete ARF encoding process needs to be applied to only 20% ~ 35% of the frames)
with negligible quality degradation (e.g., around 0.05 dB loss).
It is highly desirable for many broadcast video applications to be able to provide support for many diverse user devices, such as devices supporting different resolutions, without incurring the bitrate penalty of simulcast encoding. On the other hand, video decoding is a very complex operation, while the complexity is very dependent on the resolution of the coded video. Low power portable devices typically have very strict complexity restrictions and reduced-resolution displays. For such environments total bitrate efficiency of combined layers is an important requirement, but the bitrate efficiency of a lower layer individually, although desired, is not a requirement. In this paper, we propose a complexity constrained scalable system, based on the Reduced Resolution Update mode that enables low decoding complexity, while achieving better Rate-Distortion performance than an equivalent simulcast based system. Our system is targeted on broadcast environment with some terminals having very limited computational and power resources.