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Record W2104495777 · doi:10.1109/newcas.2005.1496683

A High Performance CABAC Encoder

2005· article· en· W2104495777 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 institutionsQueen's University
Fundersnot available
KeywordsContext-adaptive binary arithmetic codingContext-adaptive variable-length codingComputer scienceEntropy encodingEncoderHuffman codingArithmetic codingReduced instruction set computingParallel computingEncoding (memory)Adaptive codingApplication-specific integrated circuitCoding (social sciences)Computer hardwareArithmeticAlgorithmData compressionInstruction setLossless compressionMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

One key technique for improving the coding efficiency of H.264 video standard is the entropy coder, context-adaptive binary arithmetic coder (CABAC). However the complexity of the encoding process of CABAC is far higher than the table driven entropy encoding schemes such as the Huffman coding. CABAC is also bit serial and its multi-bit parallelization is extremely difficult. For a high definition video encoder, multi-giga hertz RISC processors will be needed to implement the CABAC encoder. In this paper, the authors provided efficient solutions for the arithmetic coder and the renormalizer. An FPGA implementation of the proposed scheme capable of 54 Mbps encoding rate and test results are presented. A 0.18 /spl mu/m ASIC synthesis and simulation shows 87 Mbps encoding rate utilizing an area of 0.42 mm/sup 2/.

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: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.471

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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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

Citations38
Published2005
Admission routes1
Has abstractyes

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