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Record W2130567887 · doi:10.1109/ccece.2009.5090242

The hardware architecture of a novel motion estimator with adaptive crossed quarter polar search patterns for H.264 encoding

2009· article· en· W2130567887 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 Calgary
Fundersnot available
KeywordsMotion estimationEstimatorComputer scienceQuarter-pixel motionBlock (permutation group theory)Encoding (memory)Hardware architectureComputer hardwareMotion (physics)ArchitectureAlgorithmParallel computingComputer visionArtificial intelligenceMathematicsSoftware

Abstract

fetched live from OpenAlex

The advanced algorithms and corresponding hardware architectures are very demanded for the low-cost and high-performance motion estimation solutions. A hardware implementation for a novel H.264 motion estimator, with adaptive crossed quarter polar search patterns, is presented in this paper. Design trade-offs, including search patterns and memory accesses, have been made to target at very low implementation complexity. This hardware architecture is optimized for variable block sizes utilized in H.264 motion estimation. The architecture is mapped and verified with co-design techniques. The experimental results show that the proposed hardware motion estimator can sufficiently support the real-time 4CIF @ 30fps video encoding running at 50MHz, and yield an average PSNR of −0.05dB, +0.34dB and +0.11dB when compared to the full search, diamond search and adaptive rood pattern search algorithms, respectively.

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 score0.860
Threshold uncertainty score0.297

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 head0.030
GPT teacher head0.270
Teacher spread0.240 · 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