MétaCan
Menu
Back to cohort
Record W2140083472 · doi:10.1109/icme.2011.6012194

Saliency-preserving video compression

2011· article· en· W2140083472 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
TopicVisual Attention and Saliency Detection
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceRegion of interestComputer visionArtificial intelligenceCoding (social sciences)SalientSaliency mapFrame (networking)Mathematics

Abstract

fetched live from OpenAlex

In region-of-interest (ROI) video coding, the part of the frame designated as ROI is encoded with higher quality relative to the rest of the frame. At low bit rates, coding artifacts in non-ROI parts of the frame may become salient and draw user's attention away from ROI, thereby degrading visual quality. In this paper we propose a saliency-preserving framework for ROI video coding. This approach aims at reducing attention-grabbing visual artifacts in non-ROI parts of the frame in order to keep user's attention on ROI. Experimental results indicate that the proposed method is able to improve the visual quality of ROI video at low bit rates.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.621

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)0.0010.000

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 head0.060
GPT teacher head0.260
Teacher spread0.200 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicVisual Attention and Saliency DetectionFrench-language works237,207