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The finite nearest-center quantizer is Borel with finite distortion
Statement
Let be Borel, let be a Borel probability measure on with finite second moment, and let satisfy the hypotheses of Finite measurable squared-error quantizer and nearest-center rule. The least-index nearest-center rule defines a Borel quantizer on , and its distortion is finite. Coincident centers and empty cells are allowed.
Facts & Assumptions
Given: Integers , a Borel subset , a Borel probability measure on with finite second moment, and finite centers .
Finite measurable squared-error quantizer and nearest-center rule: the nearest-center cell is the Borel domain intersected with strict inequalities against lower indices and non-strict inequalities against higher indices; distortion is the integral of squared Euclidean error.
Proof
For fixed , the difference is a continuous polynomial in the coordinates of . Therefore its strict-negative set is open and its nonpositive set is closed; the finite intersections defining each in [F1] are Borel.
The finite list of distances has a minimum at each , and the least minimizing index is unique. The tie inequalities in [F1] therefore make the cells a disjoint partition of ; the inverse image of each singleton index is relatively Borel by step 1.1, so the finite-valued index map is Borel.
For each coordinate, , since . Summing coordinates and integrating over the partition gives , so the distortion is well-defined and finite.
Depends on
Used by
- Exact finite-data Lloyd quantizer Algorithm
- Exhaustive global empirical squared-error quantizer Algorithm
Cited to discharge well-definedness by Finite measurable squared-error quantizer and nearest-center rule.
Dependency tree · two levels
2 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Robert M. Gray, Source Coding Theory, first edition (standard reference, not scraped)
- Robert Gallager, 6.450 Principles of Digital Communications I, Chapter 3: Quantization (standard reference, not scraped)
- Robert M. Gray and David L. Neuhoff, Quantization, IEEE Transactions on Information Theory 44(6) (1998) (standard reference, not scraped)