A hybrid approach of wavelet packet and directional decomposition for image compression
Bibliographic record
Abstract
Abstract In this paper, a novel image compression technique, the combination of wavelet packet transform and directional decomposition is proposed. Wavelet packet transform is an increasingly remarkable image compression approach that outperforms the standard wavelet transform in image coding. The directional filtering coding technique, one of the second‐generation image coding techniques, first introduced the concept of directional decomposition. By placing emphasis on edge detection to preserve edge information to exploit the fact that human visual systems are more sensitive to image edge features, a relatively high compression ratio can be obtained. The approach proposed in this paper decomposed an image into a low‐frequency component and a number of highfrequency components, with the edges on each high‐frequency component in its own direction. By a combined process of Cartesian coordinate rotation transform, interpolation, wavelet packet transform, and a coding algorithm, the image can be reconstructed at an improved visual quality at the same bit rate compared with the common wavelet pyramid algorithm. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 51–55, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10010
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".