HYBRID IMAGE COMPRESSION TECHNIQUES USING DWT AND NEURAL NETWORKS
Bibliographic record
Abstract
One of the most important methods for cutting the costs of digital image transmission and storage is image compression. In order to obtain large compression ratios and good image quality, this research proposes a hybrid image compression technique that combines the benefits of Neural Networks (NN) and Discrete Wavelet Transform (DWT). The input image first has to be divided down into segments at various frequencies using DWT. After being quantized, the sub-bands are put into a neural network to be further compressed. The neural network is trained to produce compressed representations with minimal data loss and to understand the statistical characteristics of the image's sub-bands. Next, a lossless or lossy compression algorithm is used to encode the compressed image data, which is then either saved or transferred. According to experimental findings, the suggested hybrid compression method performs better in terms of compression ratio and image quality than conventional DWT- and NN-based compression methods. Furthermore, by varying the neural network design and the compression settings, our method is adaptable to various compression requirements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".