A real-time wavelet-based video decoder using SIMD technology
Bibliographic record
Abstract
This paper presents a fast implementation of a wavelet-based video codec. The codec consists of motion-compensated temporal filtering (MCTF), 2-D spatial wavelet transform, and SPIHT for wavelet coefficient coding. It offers compression efficiency that is competitive to H.264. The codec is implemented in software running on a general purpose PC, using C programming language and streaming SIMD extensions intrinsics, without assembly language. This high-level software implementation allows the codec to be portable to other general-purpose computing platforms. Testing with a Pentium 4 HT at 3.6GHz (running under Linux and using the GCC compiler, version 4), shows that the software decoder is able to decode 4CIF video in real-time, over 2 times faster than software written only in C language. This paper describes the structure of the codec, the fast algorithms chosen for the most computationally intensive elements in the codec, and the use of SIMD to implement these algorithms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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".