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Record W2116084082 · doi:10.1109/mue.2011.31

Impact of Frame Loss Position on Transmitted Video Quality: Models and Improvements

2011· article· en· W2116084082 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
TopicVideo Coding and Compression Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsComputer scienceReal-time computingPacket lossNetwork packetVideo compression picture typesFrame (networking)Video qualityFrame rateDistortion (music)Residual frameVideo processingVideo trackingComputer visionComputer networkReference frameBandwidth (computing)

Abstract

fetched live from OpenAlex

This paper addresses the question of whether or not the specific packet/frame lost, and in particular, its relative position toward the I-frames influences the quality of transmitted compressed video. We focus on the QCIF size video frame, one of the most pervasive video formats across the Internet and cellular systems. Using the average Peak Signal to Noise Ratio (PSNR) of the received coded video to measure the amount of distortion, we demonstrate that the interval between the lost frame and the last I-frame does have a significant effect on the resulting quality. Further, after investigating the probability of different burst loss lengths in noisy environments, where the duration of data loss is almost constant, we propose a method to improve performance of video streaming over the noisy channels based on packet scheduling, without requiring an increase in the bit rate.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.297

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 science0.0000.000
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 head0.069
GPT teacher head0.313
Teacher spread0.244 · 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