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Record W2148503912 · doi:10.1109/iscas.1998.698814

A two-layer MPEG2-compatible video coding technique using wavelets

2002· article· en· W2148503912 on OpenAlexaff
Jinwen Zan, Wei‐Ping Zhu, M. Omair Ahmad, M. N. S. Swamy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceRingingCoding (social sciences)BitstreamSub-band codingWaveletAlgorithmCoding tree unitRinging artifactsEnhanced Data Rates for GSM EvolutionComputer visionSpeech recognitionDecoding methodsSpeech codingMathematicsImage (mathematics)

Abstract

fetched live from OpenAlex

In this paper, a two-layer hierarchical video coding technique that is compatible with MPEG2 is presented. By using a simplified version of the Mallat algorithm, a video signal is decomposed as two constituent signals, the smoothed signal comprising low-frequency content and the edge part representing the high-frequency information. The smoothed signal is then encoded into an MPEG2 video stream, while the edge information is encoded by utilizing the chain code into an MPEG2 private stream. By applying this coding strategy, the artifacts such as the blocking effect that is usually present in conventional block-based coding schemes, especially at low bit rate, and the ringing effect which may occur in a wavelets-based method, are both overcome. Simulation results show that the proposed coding scheme is capable of producing high-quality reconstructed video signals.

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)0.000
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.320
Teacher spread0.238 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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