Neural Image Compression with Regional Decoding
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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