Noncausal directional intra prediction: Theoretical analysis and simulation
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).
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".