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Record W2165892547 · doi:10.1109/ism.2009.54

Quality Assessment of Video Content for HD IPTV Applications

2009· article· en· W2165892547 on OpenAlexaff
Wei Li, Omneya Issa, Hong Liu, Filippo Speranza, Ron Renaud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsIPTVComputer scienceVideo qualityTestbedPEVQMean opinion scoreMultimediaQuality of experiencePacket lossInternet ProtocolSubjective video qualityHigh-definition televisionThe InternetQuality (philosophy)Quality of serviceBroadband networksNetwork packetBroadbandComputer networkTelecommunicationsArtificial intelligenceImage qualityWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

In the last few years, Internet Protocol Television (IPTV) has emerged as one of the major distribution technologies for broadband multimedia services. However, the delivery of High Definition Television (HDTV) services over IP networks still faces many challenges. This paper presents a study on quality assessment of HD video content for IPTV applications. Video sequences with different content types are delivered through an IPTV testbed. Subject to packet loss, their quality is evaluated with common objective video quality measurement tools in the emulated error-prone environment with respect to their content complexity. Objective criticality based on the calculation of spatio-temporal activities is examined to validate its suitability as video content complexity indicator in quality assessment. Subjective assessment is further carried out to validate the objective test results. With a good correlation between subjective Mean Opinion Score (MOS) and objective metrics, the prediction of viewer satisfaction in terms of HD video content quality in IPTV networks becomes feasible. Suggestions are also made regarding the choice of suitable measurements for more meaningful HD content classifications.

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 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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.153
GPT teacher head0.432
Teacher spread0.279 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations7
Published2009
Admission routes1
Has abstractyes

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