Bibliographic record
Abstract
With rapid increase of image resolution in modern video processing and display systems, the bandwidth and power consumption of external memory are becoming serious bottlenecks. This problem can be alleviated by high-fidelity embedded compression (EC) techniques for video frame buffers. Classic lossless or near-lossless coding methods like CALIC are ill suited for embedded systems due to their high complexity. In this work, a new, simple infra-frame EC technique based on downsampling and side-information aided upsampling is developed. Through a study of a family of downsampling schemes, an optimal one is found and analyzed for EC. This downsampling scheme gives birth to the new EC technique. The main idea is to first split an image into blocks, and then adaptively choose different down sampling patterns and upsampling methods to code/decode these blocks. For a memory bandwidth reduction of 60%, the proposed EC system can achieve PSNR above 40dB, while allowing very simple, low-cost real-time hardware realization. A noteworthy novelty of this work is compression without entropy coding. The resulting code stream is of fixed-rate, supporting random access to pixel blocks.
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 | 0.000 | 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) | 0.001 | 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".