Robust video coding over wireless channels using TRIRF inter-frame coding
Bibliographic record
Abstract
In video communication systems based on motion-compensated predictive coding, transmission errors cause spatial and temporal distortion propagation during; the reconstruction of the video sequence at the receiver. Two commonly used techniques to stop error propagation are (1) periodic refreshing by intra-frame coding and (2) retransmission. However, frequent intra-frame refreshing may be expensive in band-limited applications such as wireless video transmission. On the other hand, retransmission causes additional delay which may be intolerable in real-time applications. We present a novel video coding mode which we call transmitter receiver identical reference frame (TRIRF) based inter-frame coding. Under the assumption of the existence of a feedback channel, TRIRF-frame coding constructs a new type of reference frame from the correctly received data which is made identical both at the receiver and the transmitter. Motion estimation and compensation are based on the TRIRF-frame. Simulations show that TRIRF-frame coding prevents error propagation as effectively as intra-coding but with improve compression efficiency. We also propose a packetization scheme for the encoded video bit streams which enables rapid resynchronization of the decoder.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".