MétaCan
Menu
Back to cohort
Record W2110722135 · doi:10.1109/sips.2005.1579901

An efficient H.264 based fine-granular-scalable video coding system

2006· article· en· W2110722135 on OpenAlex
Kemal Uǧur, Panos Nasiopoulos, Rabab Ward

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 institutionsUniversity of British Columbia
Fundersnot available
KeywordsMacroblockComputer scienceCodecDiscrete cosine transformHeaderScalabilityCoding tree unitScalable Video CodingAlgorithmTransform codingReal-time computingComputer networkComputer hardwareDecoding methodsComputer visionMotion compensation

Abstract

fetched live from OpenAlex

H.264, with its superior coding efficiency and network friendly design, has emerged as the newest international video standard and is expected to become the preferred codec for video broadcasting. Presently, there is an effort to add scalability to H.264, in order to offer a solution to network congestion and bandwidth variations. Proposals range from scalable subband extension to introduction of FGS to the H.264 standard. We propose a novel structure for the FGS layer that uses 4/spl times/4 integer transform, instead of 8/spl times/8 discrete cosine transform (DCT), so that the same transform is used for both layers. We also propose a novel hierarchical algorithm to code macroblock header of FGS layer that uses less bits than the standard FGS algorithm and significantly increases the coding efficiency. When compared with the original FGS structure, the proposed structure uses 70% less bits for macroblock headers, has less complexity and has an increased PSNR of 0.7 dB on average.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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