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Record W2170200836 · doi:10.1109/icme.2011.6011880

An error resilient technique for temporal and spatial scalability

2011· article· en· W2170200836 on OpenAlex
Amir Naghdinezhad, Fabrice Labeau

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
TopicVideo Coding and Compression Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceEncoderScalabilityRobustness (evolution)Real-time computingScalable Video CodingDecoding methodsMotion compensationAlgorithm

Abstract

fetched live from OpenAlex

In video transmission systems, compressed video is delivered over channels that are not necessarily error free. Channel errors can lead to a mismatch in the encoder/decoder prediction loop which will propagate the errors to the succeeding frames. As a result, the quality of the received video at the decoder side may drop significantly. In this paper, we aim to reduce the introduced mismatch by modifying the reference frame. Our technique makes use of previously Intra coded blocks and a new leaky prediction structure in order to improve the robustness. This technique was applied on temporal and spatial scalability of scalable extension of H.264/AVC. Simulation results show the effectiveness of our scheme especially for medium and high motion sequences.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score0.261

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 science0.0010.000
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 head0.053
GPT teacher head0.290
Teacher spread0.237 · 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