Deblocking of Block-Transform Compressed Images Using Phase-Adaptive Shifted Thresholding
Bibliographic record
Abstract
Many popular image compression schemes are based on block-transform coding, a technique where images are broken into small blocks of pixels prior to transformation and compression. Block-transform coding often introduces blocking artifacts which are particularly prevalent at low bit-rates due to quantization errors. A novel algorithm for deblocking block-transform compressed images is proposed in this paper. This algorithm is based on a phase-adaptive, shifted thresholding technique that estimates the original uncompressed image as the weighted sum of shifted versions of the decompressed image subjected to a threshold. An efficient integer transform is used to construct the shifted versions of the decompressed image. The aggregation weights are obtained adaptively using the local phase moment characteristics of the underlying image content. The proposed algorithm utilizes important human perceptual characteristics to provide effective image deblocking while preserving image detail. Experimental results show that the proposed algorithm is more efficient than comparable methods and yields both subjective results and peak signal-to-noise ratio (PSNR) results comparable to existing methods.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".