Started working on reconstruction
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@ -172,3 +172,38 @@ Each measurement will be quantized with out quantizer $cal(E)$, returning a tupl
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$ K_i = cal(E)(s, m tilde(x_i)) = (k, h)_i $ <eq:smhd_quant>
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Performing the operation of @eq:smhd_quant for our whole set of measurements will yield a vector of tuples $bold(K)$.
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#pagebreak()
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=== Reconstruction
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We already demonstrated the basic principle of the reconstruction phase in section @sect:tmhd, more specifically with @fig:tmhd_example_enroll and @fig:tmhd_example_reconstruct, which show the advantage of using more than one quantizer during reconstruction.
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We will call our repeated measurement of $tilde(x)$ that is subject to a certain error $tilde(x^*)$.
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To perform reconstruction with $tilde(x^*)$, we will first need to find all $s$ quantizers for which we generated the helper data in the previous step.
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We have to distinguish two different cases for the value of $s$:
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- $s$ is odd
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- $s$ is even
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==== Even number of metrics
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If $s$ is even, we need to move our quantizer $s/2$ times some distance to the right and $s/2$ times some distance to the left.
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We can define the ideal position for the quantizer bounds based on its corresponding metric as centered around the center of the related metric.
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We can find these new bounds graphically as depicted in @fig:smhd_find_bound_graph. We first determine the x-values of the centers of a metric (here M1, as shown with the arrows). We can then place the quantizer steps with step size $Delta$ (@eq:delta) evenly spaced around these points.
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#grid(
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columns: (1fr, 0.1fr, 1fr),
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[#scale(x: 70%, y: 70%)[
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#figure(
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include("../graphics/quantizers/s-metric/2_2_find_quantizer.typ"),
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caption: [Ideal centers and bounds for the M1 quantizer]
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)<fig:smhd_find_bound_graph>]],
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[#align(center)[#align(horizon)[#text(25pt)[$arrow.r.double$]]]],
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[#scale(x: 70%, y: 70%)[
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#figure(
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include("../graphics/quantizers/s-metric/2_2_found_quantizer1.typ"),
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caption: [Quantizer for the first metric]
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)]]
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)
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