MétaCan
Menu
Back to cohort
Record W4283658061 · doi:10.1002/sdtp.15669

75‐2: The Effect of Chromatic Aberration Correction on Visually Lossless Compression

2022· article· en· W4283658061 on OpenAlex
Sanjida Sharmin Mohona, Domenic Au, Laurie M. Wilcox, Robert S. Allison

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

VenueSID Symposium Digest of Technical Papers · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsComputer scienceComputer visionChromatic aberrationDistortion (music)Artificial intelligenceChromatic scaleColor spaceLossless compressionCodecChromatic adaptationComputer graphics (images)OpticsData compressionPhysicsImage (mathematics)Computer hardwareTelecommunications

Abstract

fetched live from OpenAlex

In augmented and virtual reality (AR/VR), magnifying display lenses typically introduce pincushion distortion and transverse chromatic aberration. These distortions are corrected by pre‐processing, one side‐effect of which is disruption of the spatial correlation between color channels. As a result, the standard practice of performing color space conversion prior to image compression may introduce undesirable, visible artefacts. To assess this, we evaluated the performance of two low impairment display stream codecs on distortion corrected stereoscopic images based on converting to YCoCg color space or bypassing the color conversion.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.530

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 science0.0000.000
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 head0.004
GPT teacher head0.228
Teacher spread0.224 · 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