Fixed tile rate codec for bandwidth saving in video processors
Bibliographic record
Abstract
The paper presents an image compression circuit for bandwidth saving in video display processors. This is intra frame tile based compression algorithm offering visually lossless quality for compression rates between 1.5 and 2.5. RGB and YCbCr (4:4:4, 4:2:2 and 4:2:0) video formats are supported for 8/10 bits video signals. The Band Width Compressor (BWC) consists of Lossless Compressor (LC) and Quantization Compressor (QC) that generate output bit streams for tiles of pixels. Size of output bit stream generated for a tile by the LC may be less or greater than a required size of output memory block. The QC generates bit stream that always fits output memory block of the required size. The output bit stream generated by the LC is transmitted if its size is less than the required size of the output memory block. Otherwise, the output bit stream generated by the QC is transmitted. The LC works on pixel basis. A difference between original and predicted pixel’s values for each pixel of a tile is encoded as prefix and suffix. The prefix is encoded by means of variable length code, and suffix is encoded as is. The QC divides a tile of pixels on a set of blocks and quantizes pixels of each block independently of the other blocks. The number of quantization bits for all pixels of a block depends on standard deviation calculated over the block. A difference between pixel’s value and average value over the block is quantized and transmitted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".