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Record W2139149233 · doi:10.1109/ccece.2006.277530

A New Efficient Algorithm for Lossless Binary Image Compression

2006· article· en· W2139149233 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
TopicAdvanced Data Compression Techniques
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsLossless compressionComputer scienceData compressionImage compressionContext-adaptive binary arithmetic codingRedundancy (engineering)FacsimileAlgorithmBinary imageBinary numberArithmetic codingImage (mathematics)Image processingArtificial intelligenceMathematicsArithmeticTransmission (telecommunications)

Abstract

fetched live from OpenAlex

Binary image compression is desirable for a wide range of applications, such as digital libraries, map archives, fingerprint databases, facsimile, etc. In this paper, we present a new highly efficient algorithm for lossless binary image compression. The proposed algorithm introduces a new method, direct redundancy elimination, to efficiently exploit the two-dimensional redundancy of an image, as well as a novel dynamic context model to improve the efficiency of arithmetic coding. Simulation results show that the proposed algorithm has comparable compression ratio to JBIG standard. In many cases, the proposed algorithm outperforms the JBIG standard

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: Methods
Teacher disagreement score0.753
Threshold uncertainty score0.582

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 science0.0010.001
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 head0.009
GPT teacher head0.276
Teacher spread0.267 · 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

Citations13
Published2006
Admission routes1
Has abstractyes

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