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Record W3146621295 · doi:10.1002/jsid.1002

Subjective assessment of display stream compression for stereoscopic imagery

2021· article· en· W3146621295 on OpenAlex
Sanjida Sharmin Mohona, 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the Society for Information Display · 2021
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsComputer scienceStereoscopyComputer visionLossless compressionArtificial intelligenceCodecUncompressed videoData compressionCompression (physics)Image compressionComputer graphics (images)Image processingVideo processingComputer hardwareImage (mathematics)Video tracking

Abstract

fetched live from OpenAlex

Abstract High‐resolution display bandwidth requirements often now exceed the capacity of display link channels necessitating compression. The goal of visually lossless compression codecs such as VESA DSC 1.2 is that viewers perceive no difference between the compressed and uncompressed images, maintaining long‐standing expectations of a lossless display link. Such low impairment performance is difficult to validate as artifacts are at or below sensory threshold. We have developed a 3D version of the ISO/IEC 29170‐2 flicker paradigm and used it to compare the effects of image compression in flat images presented in the plane of the screen (2D) to compression in flat images with a disparity offset from the screen (3D). We hypothesized that differences in the location and size of the compression errors between the disparate images in the 3D case would affect their visibility. The results showed that artifacts were often less visible in 3D compared to 2D viewing. These findings have practical applications with respect to codec performance targets and algorithm development for 3D movie, animation, and virtual reality content. In particular, higher compression should be attainable in stereoscopic compared to equivalent 2D images because of increased tolerance to artifacts that are binocularly unmatched or have disparity relative to the screen.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.018
GPT teacher head0.329
Teacher spread0.311 · 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