Generalized fast index assignment for robust multiple description scalar quantizers
Bibliographic record
Abstract
Based on multiple description coding, the robust multiple description scalar quantizer (RMDSQ) was introduced to combat both packet losses and bit errors over heterogeneous networks. In the RMDSQ, the residual information in the description with bit errors is utilized to achieve graceful performance degradation with the help of the correct description. Exhaustive search and the genetic algorithm were applied to obtain a ldquoclose-to-optimumrdquo RMDSQ encoder-decoder pair. The computational complexity of designing this system is high, so that it is impractical to apply this system in applications where the training time is of primary concern. In order to simplify this design procedure, in this paper, we propose a novel generalized index assignment algorithm with low computational complexity to achieve a balanced RMDSQ with low side distortions. In the proposed algorithm, a number of parity bits are applied to achieve high robustness against both bit errors and packet losses. Experimental results show that the proposed algorithm is more robust against both packet losses and bit errors than existing algorithms.
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 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 | 0.001 |
| 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 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".