JPEG Compliant Compression for DNN Vision
Bibliographic record
Abstract
Conventional image compression techniques are mostly developed for the human visual system. However, with the extensive use of deep neural networks (DNNs), more and more images will be consumed by DNN-based intelligent machines, which makes it crucial to develop image compression techniques customized for DNN vision while being JPEG compliant. In this paper, we first propose a new distortion measure, dubbed the sensitivity weighted error (SWE). Then, we develop OptS, a DNN-oriented compression algorithm with full JPEG compatibility, which designs optimal quantization tables for DNN models based on SWE. To test the performance of our algorithm, experiments of image classification are conducted on the ImageNet dataset for two prevailing DNN models. Results demonstrate that our algorithm achieves better rate-accuracy (R-A) performance than the default JPEG. For some DNN model, the compression ratio of our algorithm can reach 8.3×1, reducing the compression rate (bits per pixel, bpp) of the default JPEG by 57.4% with no accuracy loss. Our source code is available at https://github.com/zkxufo/OptS.git.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".