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Video Quality Adjustment Model Supporting Mobility for Seamless Multimedia Service Delivery

2013· article· en· W2055992936 on OpenAlexaff
Dong Jun Suh, Seong Ju Chang

Bibliographic record

VenueApplied Mechanics and Materials · 2013
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsHandoverComputer scienceMultimediaContext (archaeology)Quality of serviceSession (web analytics)Host (biology)Mobility modelQuality of experienceService (business)Video streamingComputer networkService delivery frameworkWorld Wide Web

Abstract

fetched live from OpenAlex

Advanced multimedia computing technology is capable of providing user-oriented services for the user and the environment through context-awareness. This study seeks to provide seamless video delivery service with the focus on the user’s mobility patterns during a multimedia streaming service session. Mobility supporting technology which ensures the provision of seamless services can be classified into host mobility and user mobility. The former corresponds to host-level handoff while the latter refers to user-level handoff. In host-level handoff, the factors that directly affect the quality of video consumption are total distance between hosts, the distance for streaming resuming while user is in mobility mode as well as the screen size of the end host. The relationship among these parameters is analyzed by carrying out a user subjective assessment and an appropriate video quality model was developed, accordingly. The proposed quality model supporting seamless-mobility has a high correlation to the assessed quality and enables an adequate seamless mobility for multimedia service delivery.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.242
Teacher spread0.224 · 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.

Study designBench or experimental
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

Citations0
Published2013
Admission routes1
Has abstractyes

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