Construction of Fast Data-driven Transforms for Image Compression via Multipath Coordinate Descent on Orthogonal Matrix Manifold
Bibliographic record
Abstract
Recent research indicates that data-driven transforms can outperform the widely used separable two-dimensional discrete cosine transform (2D-DCT) in applications such as video coding. However, unlike the 2D-DCT, data-driven transforms are random matrices with no structure and do not lend themselves to fast computations. In this paper, we investigate a new approach to construct low-complexity data-driven transforms by exploiting a connection between the Givens rotation matrices and coordinate descent on the orthonormal matrix manifold. We propose a multi-path coordinate descent algorithm which is observed to produce better transform matrices than the simple coordinate descent. Our experiments with many images showed that the proposed algorithm can be used to design fast data-driven transforms which achieve a higher coding gain than the 2D-DCT in some image blocks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".