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Record W1631108712 · doi:10.1109/iwsoc.2004.11

A novel memory architecture for real-time mesh-based video motion compensation

2004· article· en· W1631108712 on OpenAlex
Mohammed S. Sayed, Wael Badawy

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 International Workshop on System-on-Chip for Real-Time Applications · 2004
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceMotion compensationFrame (networking)Reference framePixelMemory architectureFrame rateArchitectureCMOSImage warpingComputer hardwareComputer visionElectronic engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a memory architecture for real-time mesh-based video motion compensation. The architecture uses the affine transformation for warping the deformed patches in the reference frame into the undeformed patches in the current frame. The reference and current frames are stored in SRAMs generated with Virage/spl trade/ memory compiler. The proposed architecture has been prototyped, simulated and synthesized using the TSMC 0.18 /spl mu/m CMOS technology. At 100 MHz clock frequency, the proposed architecture processes one CIF video frame (i.e. 352x288 pixels) in 0.59 ms. This means it can process up to 1694 frames per second. The core area of the proposed architecture is 28.04 mm/sup 2/ and its power consumption is 31.15 mW.

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)0.001
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 score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.030
GPT teacher head0.293
Teacher spread0.263 · 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