Quantization of KLT Matrices via GMRF Modeling of Image Blocks for Adaptive Transform Coding
Bibliographic record
Abstract
Forward adaptive transform coding of images requires a codebook of transform matrices from which the best transform can be chosen for each macroblock. Codebook construction is a problem of designing a quantizer for Karhunen-Lóeve transform (KLT) matrices estimated from sample image blocks. We present a novel method for KLT matrix quantization based on a finite-lattice non-causal homogeneous Gauss-Markov random field (GMRF) model with asymmetric Neumann boundary conditions for blocks in natural images. The matrix quantization problem is solved in the GMRF parameter space, simplifying the harder problem of quantizing a large matrix subject to an orthonormality constraint to a low-dimensional vector quantization problem. Typically used GMRF parameter estimation methods such as maximum-likelihood (ML) do not necessarily maximize the coding performance of the resulting transform matrices. To this end we propose a method for GMRF parameter estimation from sample image data, which maximizes the high-rate transform coding gain. We also investigate the application of GMRF-based transforms to variable block-size adaptive transform coding.
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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 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".