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Record W2168404597 · doi:10.1109/tsp.2005.863032

Combined source and channel coding with JPEG2000 and rate-compatible low-density Parity-check codes

2006· article· en· W2168404597 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

VenueIEEE Transactions on Signal Processing · 2006
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsAlgorithmLow-density parity-check codeComputer scienceParity bitCoding gainChannel (broadcasting)Forward error correctionDecoding methodsCode rateBit error rateTurbo codeCoding (social sciences)Viterbi decoderMathematicsTelecommunicationsStatistics

Abstract

fetched live from OpenAlex

Rate-compatible low-density parity-check (RC-LDPC) codes are used to provide unequal error protection for the robust and efficient transmission of JPEG2000 compressed images over noisy channels. The total bit budget is partitioned between the source and the channel coding by using a Viterbi algorithm (VA) applied to a search trellis, and appropriate channel code rates are assigned to the source blocks. The performance of the proposed scheme is evaluated on binary symmetric channels (BSCs). Experimental results indicate that the proposed scheme compares favorably with other combined source/channel coding schemes over a variety of channel conditions and transmission bit rates. In particular, the proposed scheme outperforms similar schemes based on turbo codes and irregular repeat-accumulate codes by up to about 1.1 and 1 dB in the expected peak signal-to-noise ratio (PSNR) of reconstructed images, respectively.

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: Empirical · Consensus signal: none
Teacher disagreement score0.731
Threshold uncertainty score0.949

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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