Joint Content and Shape Adaptive Transforms Based on a GMRF Model for Coding Arbitrarily Shaped Image Segments
Bibliographic record
Abstract
It is known that shape-adaptive (SA) transform coding can outperform block-based transform coding for images. However, there is no simple way to construct optimal transforms for arbitrarily shaped image segments. We present a novel method for constructing SA transforms using a non-causal finite lattice homogeneous Gauss-Markov random field (GMRF) model. Karhunen-Loéve transform (KLT) of the GMRF model serves as a compact parameterized model for the KLT of an arbitrarily shaped segment, where the model parameters represent the image texture. In contrast to other SA transforms considered in the literature, our approach makes it possible to optimize the transform to an image segment in the sense that if the GMRF model is exact for the texture, the resulting KLT is the optimal transform for the image segment. An important part of the proposed GMRF model is a modified version of asymmetric Neumann boundary conditions designed to ensure a high transform coding gain for arbitrarily shaped image segments. By using a candidate set of GMRF parameters estimated from a training set of images, our KLT model can be used for joint content and shape adaptive transform (JCSAT) coding. Experimental results show that JCSAT coding outperforms other known SA transforms, as well as the standard 2D-DCT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".