MétaCan
Menu
Back to cohort
Record W2524652555 · doi:10.1145/2964284.2967204

INRS Audiovisual Quality Dataset

2016· article· en· W2524652555 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
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Image Processing Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsComputer scienceQuality (philosophy)Speech recognitionArtificial intelligence

Abstract

fetched live from OpenAlex

We present the INRS audiovisual quality dataset made of 160 unique configurations for audiovisual content including various media compression and network distortion parameters such as video frame rate, quantization and noise reduction parameters, and packet loss rate. The compression and network distortion parameter range values are selected to match real-time communications use cases. The H.264 video codec in 720p resolution and the AMR-WB audio codec are used for encoding video and audio streams. Thirty observers have rated the overall audiovisual quality on the Absolute Category Rating (ACR) 5-level quality scale in a controlled environment. The dataset includes MOS values, packet loss rates measured at bit stream level for both video and audio streams, compression parameters and various packet header information. We have used open source software for producing source audiovisual sequences, end-to-end streaming and a custom video player. These tools and the dataset are free to public access for research and development purposes.

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 score0.498
Threshold uncertainty score0.210

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 communication0.0000.001
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 head0.040
GPT teacher head0.378
Teacher spread0.338 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Image Processing TechniquesFrench-language works237,207