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Record W2294924997 · doi:10.1109/icce.2016.7430642

Perceptual distortion measurement in the coding unit mode selection for 3D-HEVC

2016· article· en· W2294924997 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
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceCoding (social sciences)Artificial intelligenceComputer visionMultiview Video CodingCoding tree unitData compressionResidualContext-adaptive binary arithmetic codingHuman visual system modelAlgorithmic efficiencyRate–distortion optimizationAlgorithmDecoding methodsVideo processingMathematicsVideo tracking

Abstract

fetched live from OpenAlex

3D-HEVC achieves higher compression efficiency compared with the simulcast HEVC or disparity-compensated multi-view video coding (MVC). Improved compression efficiency is highly desirable in the transmission of 3D video and its storage. The coding efficiency gain is the result of the new coding tools introduced in 3D-HEVC such as inter-view motion prediction and inter-view residual prediction. We propose to integrate a perceptual video quality metric inside the rate distortion optimization process of the 3D-HEVC. Specifically, in the coding unit (CU) mode selection process, PSNR-HVS is used as a measure for distortion. PSNR-HVS is based on the characteristics of the human visual system (HVS). Results show that the proposed approach improves the compression efficiency of the 3D-HEVC and achieves higher video quality compared with 3D-HEVC.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.139

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 science0.0010.000
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 head0.094
GPT teacher head0.290
Teacher spread0.196 · 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