Real-time implementation of full-search vector quantization on a low memory SIMD architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract only given. A significant amount of current research on vector quantization (VQ) implementations addresses increasing the speed of image encoding. This is typically accomplished by imposing structures, exploiting properties of the distance measure, or developing efficient and fast implementations. This research proposes a parallel implementation of a full-search VQ encoding algorithm using a low memory, fine grain SIMD pixel processor (SIMPil) being developed at Georgia Tech. This implementation fully exposes the available parallelism of the encoding process and exploits the processing and I/O capabilities of the processor, resulting in a system that can perform real-time image and video compression. The proposed implementation encodes a large region of the original image at once, replacing each constituent input block with its corresponding VQ codeword index. Preliminary simulation results indicate that the proposed implementation is capable of sustain real-time frame rates. In the simulation, 92058 clock cycles are required to encode a single 64/spl times/64 region. The image of Lena (256/spl times/256) requires 16 passes to be completely encoded, for a total of about 1472089 clock cycles. With a 50 MHz processor, a 256/spl times/256 image frame will be encoded in 29.4 ms, supporting a frame rate of >30 frames/sec. Three VQ implementations are compared on different hardware platforms. A prototype SIMPil implementation is being fabricated by MOSIS in 0.8 /spl mu/m CMOS.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it