Bibliographic record
Abstract
As advancements are made in technology such as AR/VR and high-resolution photography, there is a growing need for a function in image compression named regional decoding . This function lets an image be encoded as a whole, but allows for an arbitrary region to be decoded using only a small part of the bitstream. However, existing neural image compression methods lack support for this crucial functionality. In this article, we propose a novel approach called the slicing en/decoder , which addresses the need for regional decoding while maintaining performance on par with state-of-the-art methods. Our approach is based on the insight that, during the compression process, local information within pixels holds greater importance than global information. By leveraging this understanding, we divide the image into different bitstreams according to cross-boundary patterns. Consequently, for a selected region, our method can intelligently choose specific portions of the bitstreams to decode only that particular region of interest. Furthermore, we extend the application of our method to 360° image compression, allowing for efficient encoding and decoding of immersive visual content. Moreover, our proposed technique offers the capability to decode regions identically, which paves the way for future advancements in regional video decoding. Our experimental results demonstrate that our method maintains performance on par with state-of-the-art methods while providing the functionality of regional decoding . In conclusion, this article presents a significant step forward in image compression technology, offering enhanced flexibility and efficiency for emerging applications in digital media.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".