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Record W2133731319 · doi:10.1109/glocom.2009.5425996

Unequal Power Allocation for Transmission of JPEG2000 Images over Wireless Channels

2009· article· en· W2133731319 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 institutionsSimon Fraser University
Fundersnot available
KeywordsBitstreamComputer scienceJPEG 2000Coding (social sciences)Distortion (music)Transmission (telecommunications)Channel (broadcasting)Channel state informationWirelessReal-time computingComputer networkDecoding methodsImage (mathematics)TelecommunicationsComputer visionImage compressionImage processingMathematics

Abstract

fetched live from OpenAlex

In this paper, we present an unequal power allocation method that exploits the hierarchical structure of JPEG2000 coded bitstream, along with the channel state information, to assign different transmission powers to different parts of the bitstream at the coding pass level. Based on the information from the JPEG2000 bitstream, we first develop a distortion model to evaluate the distortion of the reconstructed image in terms of the power assigned to each coding pass within the bitstream. Using this model and the channel information, the powers are calculated such that the distortion of the decoded image is minimized. Simulation results show up to 7 dB improvement in the decoded image quality compared to the case of equal power allocation and up to 4 dB enhancement compared to the state-of-the-art methods proposed for the transmission of JPEG2000 streams over wireless channels.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.687
Threshold uncertainty score0.373

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 communication0.0000.001
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 head0.013
GPT teacher head0.294
Teacher spread0.281 · 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

Citations14
Published2009
Admission routes1
Has abstractyes

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