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Record W4229867752 · doi:10.1002/9781119111771.ch11

Changing the Sampling Structure of an Image

2019· other· en· W4229867752 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typeother
Languageen
FieldComputer Science
TopicAdvanced Image Processing Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBilinear interpolationUpsamplingBicubic interpolationStairstep interpolationAlgorithmLattice (music)Interpolation (computer graphics)Computer scienceMathematicsSampling (signal processing)PolynomialImage scalingComputer visionImage (mathematics)Image processingMultivariate interpolationMathematical analysisFilter (signal processing)

Abstract

fetched live from OpenAlex

There are two main applications for changing the sampling structure of an image: resizing and format conversion (or standards conversion). Resizing generally involves displaying the image on the same display but with a larger or smaller size. Format conversion involves switching between different formats such as European and North American television standards. This chapter considers three cases: upsampling to a superlattice; downsampling to a sublattice, and arbitrary lattice conversion. The most widely used interpolation filters in practice are bilinear and bicubic separable filters. The most common method in practice to convert the sampling structure of a rectangularly sampled image to another lattice is separable polynomial interpolation. This can be thought of as producing a continuous version of the signal that can then be resampled at arbitrary locations. The most frequently used polynomial interpolators are zero-order hold, linear (straight-line) interpolation and cubic interpolation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.510
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.296
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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