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Record W2073962639 · doi:10.1258/1357633001935789

The effect of transmission bandwidth on the quality of ophthalmological still and video images

2000· article· en· W2073962639 on OpenAlexaff
Oscar Cuzzani, Matthew Bromwich, Ellen E. Anderson Penno, Howard V. Gimbel

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2000
Typearticle
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsQueen's UniversityGimbel Eye CentreUniversity of Calgary
Fundersnot available
KeywordsContrast (vision)Image qualityComputer scienceImage resolutionComputer visionBandwidth (computing)Transmission (telecommunications)MegabitMedicineFluorescein angiographyDisplay resolutionArtificial intelligenceOphthalmologyImage (mathematics)TelecommunicationsDisplay deviceVisual acuity

Abstract

fetched live from OpenAlex

We have examined the minimum realtime transmission speed for video-angiography with the Rodenstock scanning laser ophthalmoscope (SLO) with respect to spatial and contrast resolution. An SLO fluorescein video-angiography sequence was recorded using high-quality media and relayed to a remote site at transmission speeds ranging from T3 (4.5 Mbit/s) to 0.125 T (197 kbit/s). Images were compared with each other subjectively by an ophthalmologist and objectively with image processing software. When compared qualitatively there was little difference between the T3 and T1 images. The T1 images scored well on clarity and contrast, while 0.5 T was satisfactory but inferior to T1. Transmission speeds below 0.5 T were inadequate. The digital analysis showed a slight difference between T3 and T1. We calculated that there was up to a 92% loss of resolution at 0.25 T and up to a 98% loss at 0.125 T. Based on our quantitative and qualitative analysis, a T3 line provided the highest bandwidth and best resolution, as expected. However, 0.5 T gave satisfactory results for realtime consultations and appears to be the minimum speed required for ophthalmic purposes, producing few motion artefacts and good resolution.

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.

How this classification was reachedexpand

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.326
Teacher spread0.312 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2000
Admission routes1
Has abstractyes

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