Hybrid image compression scheme based on wavelet transform and adaptive context modeling
Bibliographic record
Abstract
Summary form only given. We propose a hybrid image compression scheme based on wavelet transform, HVS thresholding and L/sub /spl infin//-constrained adaptive context modelling. This hybrid system combines the strengths of the wavelet transform, the HVS thresholding and the adaptive context modelling to result in a near optimal compression scheme. The wavelet transform is very powerful in localizing the global spatial and frequency correlation. The HVS model-based thresholding is designed to exploit and eliminate the wavelet coefficients insensitive to the human visual system. The context-based modelling is superior in decorrelating the local redundancy. In the scheme, the image is first decomposed into the multiresolution subimages using the orthogonal wavelet transform; each subimage corresponds to a octave band in the wavelet decomposition. The coefficients in the high-pass octave bands of the wavelet transform are then quantized through HVS frequency- and spatial model-based thresholding and vector quantization into wavelet decomposition with only significant coefficients to the HVS retained. In this HVS quantized wavelet decomposition, the coefficients insignificant to the human visual system are normalized to zero and the global spatial and frequency correlation are exploited and removed. Then the quantized subimages in the low-pass band and the remaining high-pass octave bands of each octave level are processed using the L/sub /spl infin//-constrained CALIC to de-correlate the local redundancy. It is demonstrated that the hybrid scheme is one of the best compression schemes in achieving the excellent compression rates and competitive PSNR while maintaining a small visual distortion. In comparing with the original CALIC, we were able to increase the PSNR by 0.65 dB or more and obtain bit rates 15 percent lower than the latter. We were also able to obtain competitive PSNR results against the best wavelet coders, while maintaining a smaller visual distortion. In particular, the wavelet CALIC was able to obtain 1.34 to 7.84 dB higher PSNR on the standard ISO test benchmarks than the SPIHT, one of the best wavelet coder.
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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 | 0.001 |
| Open science | 0.001 | 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".