Quality Issues of Hardware-Accelerated High-Quality Filtering on PC Graphics Hardware
Bibliographic record
Abstract
This paper summarizes several quality issues of an approach for high-quality filtering with arbitrary filter kernels on\nPC graphics hardware that has been presented previously. Since this method uses multiple rendering passes, it is prone\nto precision and range problems related to the limited precision and range of intermediate computations and the color\nbuffer. This is especially crucial on consumer-level 3D graphics hardware, where usually only eight bits are stored\nper color component. We estimate the accumulated error of several error sources, such as filter kernel quantization\nand discretization, precision of intermediate computations, and precision and range of intermediate results stored in the\ncolor buffer. We also describe two approaches for improving precision at the expense of a higher number of rendering\npasses. The first approach preserves higher internal precision over multiple passes that are forced to store intermediate\nresults in the less-precise color buffer. The second approach employs hierarchical summation for attaining higher overall\nprecision by using the available number of bits in a hierarchical fashion. Additionally, we consider issues such as the\norder of rendering passes that is crucial for avoiding potential range problems, and a variant of hardware-accelerated\nhigh-quality filtering that is able to reduce the number of passes by four for filtering single-valued data, thus improving\nboth performance and precision.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".