FPGA implementation of low-power and high-PSNR DCT/IDCT architecture based on adaptive recoding CORDIC
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".