Joint source-channel decoding of JPEG2000 images with unequal loss protection
Bibliographic record
Abstract
This thesis proposes the joint decoding of JPEG2000 bitstreams and Reed-Solomon codes in the context of unequal loss protection. Using error resilience features of JPEG2000 bitstreams, the joint decoder helps to restore the erased symbols when the Reed-Solomon decoder fails to retrieve them on its own. The proposed joint decoding technique can deliver significant quality gain, though the process is often computationally exhaustive. To reduce the extra decoding time, we provide three solutions. The first two solutions employ smaller codeblocks and sub-coding-pass error localization to accelerate the process. Furthermore, we show how transmitting a relatively small amount of side information with high reliability may help the joint decoder by reducing the size of the search space and bypassing some of the JPEG2000 decoding iterations needed to verify the correctness of the restored source information. The improved joint decoder can perform significantly faster than the basic one.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".