Noncausal directional intra prediction: Theoretical analysis and simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the theoretical analysis and simulation of noncausal directional intra prediction for image and video coding, where noncausal pixels, that is, pixels inside, below, or to the right of the target block, are used to predict the block, at the cost of extra bits to code those noncausal reference pixels. The proposed method generalizes the conventional causal intra prediction. The optimal number and locations of noncausal reference pixels are determined by minimizing the total differential entropies of the prediction residuals and the noncausal pixels. In order to obtain the differential entropy, a statistical image model is used to derive the autocorrelation of the residuals. In addition, the optimal sinusoidal approximations to transform the residuals are obtained by maximizing the coding gain. In the simulation, the optimal noncausal reference pixels and transforms of 4 × 4 and 8 × 8 blocks are identified for up to 33 directions. The results could provide insights for the design of more advanced directional intra prediction for the High Efficiency Video Coding (HEVC).
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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 | 0.000 | 0.000 |
| 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 it