Enhancing Image Quality by Reducing Compression Artifacts Using Dynamic Window Swin Transformer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Video/image compression codecs utilize the characteristics of the human visual system and its varying sensitivity to certain frequencies, brightness, contrast, and colors to achieve high compression. Inevitably, compression introduces undesirable visual artifacts. As compression standards improve, restoring image quality becomes more challenging. Recently, deep learning based models, especially transformer-based image restoration models, have emerged as a promising approach for reducing compression artifacts, demonstrating very good restoration performance. However, all the proposed transformer based restoration methods use a same fixed window size, confining pixel dependencies in fixed areas. In this paper, we propose a new and unique image restoration method that addresses the shortcoming of existing methods by first introducing a content adaptive dynamic window that is applied to self-attention layers which in turn are weighted by our channel and spatial attention module utilized in Swin Transformer to mainly capture long and medium range pixel dependencies. In addition, local dependencies are further enhanced by integrating a CNN based network inside the Swin Transformer Block to process the image augmented by our self-attention module. Performance evaluations using images compressed by one of the latest compression standards, namely the Versatile Video Coding (VVC), when measured in Peak Signal-to-Noise Ratio (PSNR), our proposed approach achieves an average gain of 1.32dB on three different benchmark datasets for VVC compression artifacts reduction. Additionally, our proposed approach improves the visual quality of compressed images by an average of 2.7% in terms of Video Multimethod Assessment Fusion (VMAF).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it