4,121 research outputs found
Motion-Compensated Coding and Frame-Rate Up-Conversion: Models and Analysis
Block-based motion estimation (ME) and compensation (MC) techniques are
widely used in modern video processing algorithms and compression systems. The
great variety of video applications and devices results in numerous compression
specifications. Specifically, there is a diversity of frame-rates and
bit-rates. In this paper, we study the effect of frame-rate and compression
bit-rate on block-based ME and MC as commonly utilized in inter-frame coding
and frame-rate up conversion (FRUC). This joint examination yields a
comprehensive foundation for comparing MC procedures in coding and FRUC. First,
the video signal is modeled as a noisy translational motion of an image. Then,
we theoretically model the motion-compensated prediction of an available and
absent frames as in coding and FRUC applications, respectively. The theoretic
MC-prediction error is further analyzed and its autocorrelation function is
calculated for coding and FRUC applications. We show a linear relation between
the variance of the MC-prediction error and temporal-distance. While the
affecting distance in MC-coding is between the predicted and reference frames,
MC-FRUC is affected by the distance between the available frames used for the
interpolation. Moreover, the dependency in temporal-distance implies an inverse
effect of the frame-rate. FRUC performance analysis considers the prediction
error variance, since it equals to the mean-squared-error of the interpolation.
However, MC-coding analysis requires the entire autocorrelation function of the
error; hence, analytic simplicity is beneficial. Therefore, we propose two
constructions of a separable autocorrelation function for prediction error in
MC-coding. We conclude by comparing our estimations with experimental results
Planning in POMDPs Using Multiplicity Automata
Planning and learning in Partially Observable MDPs (POMDPs) are among the
most challenging tasks in both the AI and Operation Research communities.
Although solutions to these problems are intractable in general, there might be
special cases, such as structured POMDPs, which can be solved efficiently. A
natural and possibly efficient way to represent a POMDP is through the
predictive state representation (PSR) - a representation which recently has
been receiving increasing attention. In this work, we relate POMDPs to
multiplicity automata- showing that POMDPs can be represented by multiplicity
automata with no increase in the representation size. Furthermore, we show that
the size of the multiplicity automaton is equal to the rank of the predictive
state representation. Therefore, we relate both the predictive state
representation and POMDPs to the well-founded multiplicity automata literature.
Based on the multiplicity automata representation, we provide a planning
algorithm which is exponential only in the multiplicity automata rank rather
than the number of states of the POMDP. As a result, whenever the predictive
state representation is logarithmic in the standard POMDP representation, our
planning algorithm is efficient.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
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