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Record W1973676375 · doi:10.1109/acssc.2013.6810465

On the use of SSIM in HEVC

2013· article· en· W1973676375 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 Waterloo
Fundersnot available
KeywordsCodecComputer scienceCoding (social sciences)Multiview Video CodingPerceptionAlgorithmic efficiencyVideo qualityAlgorithmComputer visionVideo processingVideo trackingComputer hardwareMathematicsEngineering

Abstract

fetched live from OpenAlex

The Structural SIMilarity (SSIM) index has been attracting an increasing amount of attention recently in the video coding community as a perceptual criterion for testing and optimizing video codecs. Meanwhile, the arrival of the new MPEG-H/H.265 High Efficiency Video Coding (HEVC) standard creates new opportunities and challenges in perceptual video coding. In this paper, we first elaborate what are the attributes that make SSIM a good candidate for perception-based development of HEVC and future video coding standards for both testing and optimization purposes. We then address the computational issues in practical applications of SSIM in HEVC, in particular the trade-off between efficient computation and accurate estimation of SSIM when working with video codecs that have sophisticated block partitioning structures and aim for encoding videos with a wide range of spatial resolutions.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.109

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 head0.090
GPT teacher head0.237
Teacher spread0.146 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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