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Record W2952944171 · doi:10.48550/arxiv.1803.04629

3D Video Quality Metric for 3D Video Compression

2018· preprint· en· W2952944171 on OpenAlexaff
Amin Banitalebi-Dehkordi, Mahsa T. Pourazad, Panos Nasiopoulos

Bibliographic record

VenuearXiv (Cornell University) · 2018
Typepreprint
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCodecComputer scienceMetric (unit)EncoderMean opinion scoreArtificial intelligenceENCODEVideo qualityComputer visionEncoding (memory)View synthesisData compressionSubjective video qualityMultiview Video CodingImage qualityVideo processingVideo trackingImage (mathematics)Rendering (computer graphics)

Abstract

fetched live from OpenAlex

As the evolution of multiview display technology is bringing glasses-free 3DTV closer to reality, MPEG and VCEG are preparing an extension to HEVC to encode multiview video content. View synthesis in the current version of the 3D video codec is performed using PSNR as a quality metric measure. In this paper, we propose a full- reference Human-Visual-System based 3D video quality metric to be used in multiview encoding as an alternative to PSNR. Performance of our metric is tested in a 2-view case scenario. The quality of the compressed stereo pair, formed from a decoded view and a synthesized view, is evaluated at the encoder side. The performance is verified through a series of subjective tests and compared with that of PSNR, SSIM, MS-SSIM, VIFp, and VQM metrics. Experimental results showed that our 3D quality metric has the highest correlation with Mean Opinion Scores (MOS) compared to the other tested metrics.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.004
Research integrity0.0010.001
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 head0.159
GPT teacher head0.282
Teacher spread0.123 · 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; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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