MétaCan
Menu
Back to cohort
Record W2000525729 · doi:10.1109/isbmsb.2010.5463161

Evaluation of H.264/AVC error resilience in HD IPTV applications

2010· article· en· W2000525729 on OpenAlexaff
Wei Li, Omneya Issa, Hong Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsIPTVComputer scienceVideo qualityDecoding methodsHigh-definition televisionDigital subscriber lineChannel (broadcasting)Scalable Video CodingComputer networkReal-time computingTelecommunicationsArtificial intelligenceMotion compensation

Abstract

fetched live from OpenAlex

The delivery of High Definition Television (HDTV) over IP networks, namely the HD IPTV, has emerged as one of the major distribution and access techniques for broadband multimedia services. IPTV adopts H.264/AVC as its coding standard due to its high video compression efficiency as well as powerful error resilience features. This paper presents studies on some of these features applied to HD IPTV applications. A test system is deployed to simulate the delivery of HD video over a DSL based IPTV network. Effects of error resilience of slicing and Instantaneous Decoding Refreshing (IDR) features on video quality are examined in both channel non-impaired and channel impaired with burst noise circumstances. Based on the acquired results, optimal slice size was obtained for HD video transmission over an impaired channel. The quality of experience related to the IDR interval in combating error propagation was also characterized.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.336
Teacher spread0.287 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicVideo Coding and Compression TechnologiesFrench-language works237,207