Ultra High Fidelity Deep Image Decompression With l∞-Constrained Compression
| Autor: | Xiaolin Wu, Xi Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Compression artifact
Pixel Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Iterative reconstruction Computer Graphics and Computer-Aided Design WebP Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering Codec 020201 artificial intelligence & image processing Algorithm Software Image restoration Decoding methods Image compression |
| Zdroj: | IEEE Transactions on Image Processing. 30:963-975 |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2020.3040074 |
| Popis: | We propose a novel asymmetric image compression system of light $\ell _\infty $ -constrained predictive encoding and heavy-duty CNN-based soft decoding. The system achieves superior rate-distortion performances over the best of existing image compression methods, including BPG, WebP, FLIF and recent CNN codecs, in both $\ell _{2}$ and $\ell _\infty $ error metrics, for bit rates near or above the threshold of perceptually transparent reconstruction. These remarkable coding gains are made by deep learning for compression artifact removal. A restoration CNN is designed to map a lossy compressed image to its original. Its unique strength is to enforce a tight error bound on a per pixel basis. As such, no small distinctive structures of the original image can be dropped or distorted, even if they are statistical outliers that are otherwise sacrificed by mainstream CNN restoration methods. |
| Databáze: | OpenAIRE |
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