Low-power data-dependent 8×8 DCT/IDCT for video compression
Bibliographic record
Abstract
Traditional fast discrete cosine transform (DCT)/inverse DCT (IDCT) algorithms have focused on reducing arithmetic complexity and have fixed run-time complexities regardless of the input. Recently, data-dependent signal processing has been applied to the DCT/IDCT. These algorithms have variable run-time complexities. A two-dimensional 8×8 low-power DCT/IDCT design is implemented using VHDL by applying the data-dependent signal processing concept onto the traditional fixed-complexity fast DCT/IDCT algorithm. To reduce power, the design is based on Loeffler's fast algorithm, which uses a low number of multiplications. On top of that, zero bypassing, data segmentation, input truncation and hardwired canonical sign-digit (CSD) multipliers are used to reduce the run-time computation, hence reducing the switching activities and the power. When synthesised using CMC 0.18-μm 1.6 V CMOSP technology, the proposed FDCT/IDCT design consumes 8.94/9.54 mW, respectively, with a clock frequency of 40 MHz and a processing rate of 320 M sample/s. This design features lower dynamic power consumption per sample, i.e. it is more power-efficient than other previously reported high-performance FDCT/IDCT designs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".