An affine-based algorithm and SIMD architecture for video compression with low bit-rate applications
Bibliographic record
Abstract
This paper presents a new affine-based algorithm and SIMD architecture for video compression with low bit rate applications. The proposed algorithm is used for mesh-based motion estimation and it is named mesh-based square-matching algorithm (MB-SMA). The MB-SMA is a simplified version of the hexagonal matching algorithm [1]. In this algorithm, right-angled triangular mesh is used to benefit from a multiplication free algorithm presented in [2] for computing the affine parameters. The proposed algorithm has lower computational cost than the hexagonal matching algorithm while it produces almost the same peak signal-to-noise ratio (PSNR) values. The MB-SMA outperforms the commonly used motion estimation algorithms in terms of computational cost, efficiency and video quality (i.e., PSNR). The MB-SMA is implemented using an SIMD architecture in which a large number of processing elements has been embedded with SRAM blocks to utilize the large internal memory bandwidth. The proposed architecture needs 26.9 ms to process one CIF video frame. Therefore, it can process 37 CIF frames/s. The proposed architecture has been prototyped using Taiwan Semiconductor Manufacturing Company (TSMC) 0.18-/spl mu/m CMOS technology and the embedded SRAMs have been generated using Virage Logic memory compiler.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".