Low bit-rate image coding via local random down-sampling
Bibliographic record
Abstract
A common practice in low bit-rate image/video compression is uniform spatial down-sampling at the encoder and upsampling at the decoder. The down-sampling is performed in conjunction with deterministic low-pass filtering (e.g., Gaussian or the alike) to prevent aliasing. The down-sampled image is compressed and decompressed as usual; the upsampling is treated as an image restoration problem. In this paper, we show that the rate-distortion performance of the above low bit-rate image coding system can be improved, if the deterministic low-pass down-sampling filter is replaced by a random convolution kernel. The resulting down-sampled image is a two-dimensional array of local random measurements; this smaller image is still compressible in most cases. Accordingly, the decoder recovers the image from these local random measurements in the framework of compressive sensing. Theoretical analysis is conducted to support the superior performance of the proposed new method over its predecessors, and it is corroborated by our simulation results. At low to medium bit rates, the new method outperforms not only JPEG 2000 but also our earlier low bit-rate image codec CADU, with clear advantages over the competing methods in the reconstruction of high frequency features. In addition, the new method retains the system advantages of low encoder complexity and standard compliance as in CADU.
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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 | 0.000 |
| 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".