Efficiency evaluation of different wavelets for image compression
Bibliographic record
Abstract
In this paper, application to image processing and image compression using the discrete wavelet transform (DWT) is presented. We show the impact of the spectral distribution of the images on the quality of the image compression technique. Four families of wavelets are considered: 1) Bi-orthogonal, 2) Daubechies, 3) Coiflet and 4) Symlet. Since the good basis wavelet recommended for DWT compressor may depend on the choice of test images, we consider three test images with different but moderate spectral activities. We then evaluate the performance of the four wavelets families on each test image. A comparative results for several wavelets used in DWT compression techniques are presented using the peak signal to noise ratio (PSNR) and compression ratio (CR) as a measure of quality. Finally, we present the comparative result according to PSNR versus CR for four families of wavelets, showing that bior4.4 yields a better performance than the other Wavelets in terms of tradeoff between PSNR and CR.
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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 | 0.002 | 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".