Learned Image Compression with Inception Residual Blocks and Multi-Scale Attention Module
Bibliographic record
Abstract
Recently, deep learning-based image compression methods have achieved superior performance compared to traditional methods. However, the complexity of the leading scheme is still quite high in both the core network and the entropy coding. In this paper, we propose two efficient modules. First, we adopt an inception residual block (IRB) in the core network, which has lower complexity than previous non-local attention module and concatenated residual blocks. Second, we employ a multi-scale attention module (MSAM), which aggregates features from three different scales to capture the global information. The output of MSAM is used as importance map to guide bits allocation. In addition, the simple Gaussian mixture model is used in the entropy coding, instead of more complicated models. Experimental results demonstrate that the encoding and decoding of our method are about 17 times faster than the state-of-the-art method. Although the R-D performance drops slightly, the performance is still better than H.266/VVC (4:4:4) and other recent learning-based methods.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".