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Record W2086161639 · doi:10.1109/isspit.2006.270856

An Efficient Multiple Description Coding Scheme for the Scalable Extension of H.264/AVC (SVC)

2006· article· en· W2086161639 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 institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceScalable Video CodingCodecScalabilityDecoding methodsMultiple description codingCoding (social sciences)Redundancy (engineering)Packet lossVideo qualityStandardizationContext-adaptive binary arithmetic codingReal-time computingQuality of serviceComputer networkNetwork packetComputer architectureData compressionAlgorithmComputer hardwareMathematics

Abstract

fetched live from OpenAlex

The demand for efficient scalable video codecs has constantly been on the rise in response to the increase in the variety of services and QoS requirements in multimedia networks. Existing standardization efforts, such as the scalable video coding extension of the H.264/AVC standard, do not offer efficient error resilient protection for all the different levels of video enhancement. We developed a multiple description scalable video coding technique that offers complementary and independently decodable descriptions, offering acceptable video quality even if only one of them is successfully received. Performance evaluations show that our scheme delivers an average improvement of 5 dB for single channel decoding and an improvement of 2 dB on average for packet loss simulations when compared with the UXP protected SD-SVC and the multiple-description motion compensated temporal filtering (MD-MCTF) scheme at comparable redundancy levels

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.255

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 science0.0010.000
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 head0.036
GPT teacher head0.255
Teacher spread0.219 · 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

Quick stats

Citations16
Published2006
Admission routes1
Has abstractyes

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