Standard-compliant multiple description image coding by spatial multiplexing and constrained least-squares restoration
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
We propose a practical standard-compliant multiple description (MD) image coding technique. Multiple descriptions of an image are generated in the spatial domain by an adaptive prefiltering and uniform down sampling process. The resulting side descriptions are conventional square sample grids that are interleaved with one the other. As such each side description can be coded by any of the existing image compression standards. A side decoder reconstructs the input image by first decompressing the down-sampled image and then solving a least-squares inverse problem, guided by a two-dimensional windowed piecewise autoregressive model. The central decoder is algorithmically similar to the side decoder, but it improves the reconstruction quality by using received side descriptions as additional constraints when solving the underlying inverse problem. Compared with its predecessors the proposed image MD technique offers the lowest encoder complexity, complete standard compliance, competitive rate-distortion performance, and superior subjective quality.
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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 | 0.002 |
| Open science | 0.000 | 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