MétaCan
Menu
Back to cohort
Record W2059854504 · doi:10.1145/1631272.1631417

Compressed domain spatial adaptation for H.264 video

2009· article· en· W2059854504 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 Ottawa
Fundersnot available
KeywordsComputer scienceBitstreamAdaptation (eye)SlicingEncoderExploitReal-time computingMetadataScalable Video CodingUncompressed videoVideo compression picture typesMotion compensationVideo trackingVideo processingComputer hardwareComputer visionComputer graphics (images)Decoding methodsAlgorithm

Abstract

fetched live from OpenAlex

In this paper, we present a metadata-based compressed-domain spatial adaptation scheme for H.264/AVC video. We have enhanced the H.264/AVC encoder with our proposed adaptation strategies in order to reduce video size by cropping individual frames in an intermediary node prior to transmitting that video to heterogeneous devices. In this regard, we exploit the sliced architecture of the video frames within the first version of the H.264/AVC specification and devise different slicing strategies. The compressed-domain bitstream modification is performed at the intermediary nodes, avoiding the need for any cascaded operations. Here, we briefly present our adaptation scheme as well as evaluation results showing the effectiveness of the slicing strategies on the bitrate reduction and processing time. A comparison of our approach with an existing cropping scheme is also presented.

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: Methods · Consensus signal: none
Teacher disagreement score0.760
Threshold uncertainty score0.332

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 head0.028
GPT teacher head0.258
Teacher spread0.230 · 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