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Record W7132976544

HVS-based shape adaptive wavelet colour image coding

2003· dissertation· W7132976544 on OpenAlexfundno aff
Che Bian

Bibliographic record

VenueTSpace · 2003
Typedissertation
Language
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsWaveletHuman visual system modelWavelet transformCoding (social sciences)JPEGPattern recognition (psychology)JPEG 2000Image quality
DOInot available

Abstract

fetched live from OpenAlex

This thesis is concerned with a shape adaptive wavelet image coding scheme based on Human Visual System (HVS) model to code arbitrarily shaped objects or regions in still colour images or video sequences. The main difference between the proposed scheme and JPEG 2000 is that the former codes the given image through object by object while the later through tiling. A wavelet transform for directly coding the arbitrarily shape objects is implemented. A contrast sensitivity-based embedded zerotree wavelet (CS-EZW) is developed. Through experimental testing, compared with the other coders, including JPEG, shape adaptive DWT, this proposed coding system gives better results in terms of visual quality of reconstructed images. The performance of this system is evaluated by using three evaluation criteria, including peak signal-to-noise ratio (PSNR), viewing by subjects and Frequency Shell Correlation (FSC).

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.360
Teacher spread0.326 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreMethods

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
Published2003
Admission routes1
Has abstractyes

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