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Record W2169662561 · doi:10.1109/tbc.2007.891700

Video Quality Metric for Bit Rate Control via Joint Adjustment of Quantization and Frame Rate

2007· article· en· W2169662561 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 Broadcasting · 2007
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsQuantization (signal processing)Video qualityRate–distortion optimizationComputer scienceMetric (unit)Computer visionInter frameArtificial intelligenceMathematicsFrame (networking)Block-matching algorithmReference frameVideo trackingVideo processingTelecommunications

Abstract

fetched live from OpenAlex

The purpose of this study is to propose a quality metric of video encoded with variable frame rates and quantization parameters suitable for mobile video broadcasting applications. As a first step, experiments are conducted to assess the subjective quality of video sequences encoded with variable frame rates and quantization parameters. Resulting experimental data show that for the purpose of video rate control, optimization using the classical PSNR does not match up to that of subjective quality data. The second step bridges this gap between PSNR and subjective quality data by constructing a new quality metric that accounts for both encoding parameters (quantization and frame rate), and intrinsic video sequence characteristics (motion speed). The average correlation coefficient for five video sequences tested is as high as 0.93 with the proposed metric, in contrast with the PSNR's 0.70

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.051
GPT teacher head0.300
Teacher spread0.249 · 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