Achieving H.264/AVC performance using distributed video coding combined with super-resolution
Bibliographic record
Abstract
Distributed Video Coding (DVC) is an emerging video coding paradigm for the systems that require encoders having low complexity that are supported by decoders having high complexity as would be required for, say, real-time video capture and streaming from one mobile phone to display on another. Under the assumption of an error-free transmission channel, the coding efficiency of current DVC systems is still below that of the latest conventional video codecs, such as H.264/AVC. To increase coding efficiency we propose in this paper that either every second Key frame or every Wyner-Ziv frame is downsampled by a factor of two in both dimensions prior to encoding and subsequent transmission. However, this would necessitate upsampling coupled with interpolation at the decoder. Simple interpolation (e.g., bilinear or FIR filter) would not suffice since high-frequency (HF) spatial image content would be missing. Instead, we propose the incorporation of a super-resolution (SR) technique that is based upon using example High Resolution images with content that are specific to the Low Resolution scene that needs its HF content to be recovered. The example-based scene-specific SR technique will add computational complexity to the decoder side of the DVC system, which is allowable within the DVC framework. Rate-distortion curves will show that this novel combination of SR with DVC improves the system performance by up to several decibels as measured by the PSNR, and can actually exceed the performance of an H.264/AVC codec, using GOP=IP, for some video sequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".