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Record W2139402936 · doi:10.1109/icip.2005.1530568

ASIC and FPGA implementations of H.264 DCT and quantization blocks

2005· article· en· W2139402936 on OpenAlexaff
Roman C. Kordasiewicz, Shahram Shirani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsQuantization (signal processing)Discrete cosine transformApplication-specific integrated circuitField-programmable gate arrayComputer scienceVirtexPixelCMOSTrellis quantizationData compressionComputer hardwareEmbedded systemAlgorithmParallel computingImage compressionElectronic engineeringArtificial intelligenceImage processingImage (mathematics)Engineering

Abstract

fetched live from OpenAlex

In the search for ever better and faster video compression standards H.264 was created. With it arose the need for hardware acceleration of its very computationally intensive parts. To address this need, this paper proposes two sets of architectures for the integer discrete transform (DCT) and quantization blocks from H.264. The first set of architectures for the DCT and quantization were optimized for area, which resulted in transform and quantizer blocks that occupy 294 and 1749 gates respectively. The second set of speed optimized designs has a throughput anywhere from 11 to 2552 M pixels/s. All of the designs were synthesized for Xilinx Virtex 2-Pro and 0.18/spl mu/m TSMC CMOS technology, as well as the combined DCT and quantization blocks went through comprehensive place and route flow.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.003

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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations40
Published2005
Admission routes1
Has abstractyes

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