Transmit rate reduction of Wyner-Ziv video coding (WZVC) using multiple uncorrelated wireless receivers
Bibliographic record
Abstract
Wyner-Ziv video coding (WZVC) is very attractive for low power wireless video sensors. In this paper we show that how the transmission bit-rate of WZVC can be further reduced when multiple uncorrelated wireless receiving points are deployed at the decoder. This is different from classical SIMO in the sense of cross layer coding is deployed. When the wireless channel degrades, decoder is fed with an optimum parities from the available parities to find the one that enables successful decoding. The optimum parity is computed from the multiple received signals using a matrix decomposition method, where the video image properties are incorporated with the wireless channel impairments. We compare the video quality (PSNR) with respect to the bit rate and the wireless channel SNR. The simulation results show remarkable improvement of PSNR even at low transmission rate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".