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Record W2156291710 · doi:10.1109/tip.2003.814252

Removing the blocking artifacts of block-based DCT compressed images

2003· article· en· W2156291710 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 Image Processing · 2003
Typearticle
Languageen
FieldComputer Science
TopicImage and Signal Denoising Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDiscrete cosine transformBlock (permutation group theory)Computer visionArtificial intelligenceSmoothingPixelQuantization (signal processing)Classification of discontinuitiesDiscontinuity (linguistics)Computer scienceBlocking (statistics)Blocking effectTransform codingColor Cell CompressionEnhanced Data Rates for GSM EvolutionMathematicsAlgorithmImage compressionImage processingImage (mathematics)

Abstract

fetched live from OpenAlex

One of the major drawbacks of the block-based DCT compression methods is that they may result in visible artifacts at block boundaries due to coarse quantization of the coefficients. We propose an adaptive approach which performs blockiness reduction in both the DCT and spatial domains to reduce the block-to-block discontinuities. For smooth regions, our method takes advantage of the fact that the original pixel levels in the same block provide continuity and we use this property and the correlation between the neighboring blocks to reduce the discontinuity of the pixels across the boundaries. For texture and edge regions, we apply an edge-preserving smoothing filter. Simulation results show that the proposed algorithm significantly reduces the blocking artifacts of still and video images as judged by both objective and subjective measures.

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)0.001
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.278
Teacher spread0.254 · 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