Development Of New Fractal And Non-Fractal Deep Residual Networks For Deblocking Of Jpeg Decompressed Images
Bibliographic record
Abstract
The JPEG compression scheme introduces blocking artifacts when the images are decompressed. JPEG image deblocking schemes based on deep neural networks map a JPEG decompressed image to its corresponding deblocked image. Employing a residual block that is capable of generating a rich set of high frequency residual features in a deep JPEG image deblocking network can improve its representational capability, and therefore, enhance the network performance. In this paper, we propose two residual blocks that generate rich high frequency residual features. The first residual block generates features from the high frequency component of its input signal in addition to generating conventional hierarchical residual features using convolutional operations. The second one is a fractal residual block that is developed by replacing the conventional convolutions in the first block by the block itself. The two proposed residual blocks are, respectively, used in recursive (non-fractal) and non-recursive (fractal) neural networks for the task of JPEG deblocking. The results of the experiments performed on the two proposed deblocking networks show their performance superiority over the respective state-of-the-art deblocking networks.
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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 | 0.000 | 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 | 0.001 |
| 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".