Interframe coding using deformable triangles of variable size
Bibliographic record
Abstract
Motion compensation using deformable triangle patches has been successfully used for low bit rate coding of videophone sequences. They were also shown to be particularly efficient for interframe coding of MRI sequences, for which the difference between image slices can be well modeled by affine deformations. Regular triangular meshes were used in previous work. In this paper we present a quadtree decomposition algorithm to generate a triangle mesh for which smaller triangles are used in image areas where the motion or deformation is more complex. The decomposition algorithm uses a strategy based on rate-distortion theory. The cost of using a new grid point, i.e., the bit rate, is compared to the reduction in prediction error energy that would result if that grid point is used. Grid points are recursively added to areas where the reduction in prediction error is more significant. Results show that using variable size triangular patches increases the SNR of the motion-compensated image by 0.5 to 0.75 dB while reducing the number of grid points by 15 to 25% when compared to a regular triangular mesh.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".