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Record W1921940893 · doi:10.1109/ccece.2001.933568

Implementation of MPEG system target decoder

2002· article· en· W1921940893 on OpenAlexaff
Maryam Azimi, Panos Nasiopoulos, Rabab Ward

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceDecoding methodsArithmetic underflowMultiplexerMultiplexingMPEG-2Real-time computingSoft-decision decoderVideo decoderEmbedded systemComputer hardwareAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

The MPEG-2 system standard provides methods for multiplexing a number of elementary MPEG streams into a single system stream. It also defines methods to maintain the synchronization and timing of compressed streams. This is achieved by exact definitions of the times at which data arrive to the decoder, timing of data flow in the decoder and timing of decoding and presentation events. For this purpose, the standard defines a conceptual model for a target decoder, called "system target decoder" (STD), which is used to model the decoding process. System streams generated by the multiplexer should comply with the specifications imposed by the STD model to guarantee the normal operations of real time decoding and presentation process. Therefore, this model is necessary during the construction and verification of system streams. The multiplexer should observe the behavior of STD to ensure that decoder buffers will not overflow or underflow due to encoding or multiplexing issues when receiving the system stream. To achieve this, the scheduler that coordinates the multiplexing order of system packs should consider the monitored information from STD as one of the scheduling control parameters to follow the specifications imposed by STD. This paper describes the theoretical principles, design considerations and architecture of program and transport STDs. The implementations of these target decoders in a software package for verification of MPEG system streams is presented. This implementation uses Microsoft DirectShow. The results of decoding some sample system streams are also presented.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.022
GPT teacher head0.272
Teacher spread0.250 · 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 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

Citations6
Published2002
Admission routes1
Has abstractyes

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