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Record W2286327216 · doi:10.1109/fpt.2015.7393139

FPGA implementation of low-power and high-PSNR DCT/IDCT architecture based on adaptive recoding CORDIC

2015· article· en· W2286327216 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
TopicDigital Filter Design and Implementation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiscrete cosine transformComputer scienceCORDICField-programmable gate arrayAdderArchitectureData compressionComputer architectureComputer hardwareEmbedded systemParallel computingAlgorithmLatency (audio)Artificial intelligenceImage (mathematics)

Abstract

fetched live from OpenAlex

The discrete cosine transform (DCT) and its inverse (IDCT) are widely used in image and video compression standards. In this paper, we propose a novel unified architecture for DCT and IDCT based on adaptive recoding coordinate rotation digital computer (ARC). The proposed architecture requires two types of ARC rotators. In addition, an efficient adder and shifter-based scale factor approximation is used in the proposed architecture. To verify the function and evaluate the performance, the proposed architecture is validated on a Virtex 5 FPGA development platform. Under DCT-only mode, compared with the proposed architecture, a state-of-the-art DCT architecture uses 12% more hardware resources, increases the critical path delay by 7.12%, consumes 10.1% more power and decreases 4.8 dB in PSNR. Under DCT/IDCT mode, the latest unified DCT/IDCT architecture has a factor of 2.17-fold in latency, needs 74.9% more hardware resources and dissipates 52.5% more power when compared to the proposed architecture. In addition, PSNR of the proposed architecture is better by 2 dB.

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 score0.911
Threshold uncertainty score0.409

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 science0.0000.000
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 head0.036
GPT teacher head0.285
Teacher spread0.249 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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