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
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.
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 | 0.001 | 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