Name
Abstract | ||
|---|---|---|
In various Computer Vision and Signal Processing applications, noise is typically perceived as a drawback of the image capturing system that ought to be removed. We, on the other hand, claim that image noise, just as texture, is important for visual perception and, therefore, critical for lossy compression algorithms that tend to make decompressed images look less realistic by removing small image details. In this paper we propose a physically and biologically inspired technique that learns a noise model at the encoding step of the compression algorithm and then generates the appropriate amount of additive noise at the decoding step. Our method can significantly increase the realism of the decompressed image at the cost of few bytes of additional memory space regardless of the original image size. The implementation of our method is open-sourced and available at this https URL |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Byte,Signal processing,Pattern recognition,Lossy compression,Computer science,Image noise,Artificial intelligence,Decoding methods,Data compression,Image resolution,Encoding (memory) |
DocType | Volume | Citations |
Journal | abs/1803.09165 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Renata Khasanova | 1 | 14 | 2.44 |
| Jan Wassenberg | 2 | 40 | 4.68 |
| Jyrki Alakuijala | 3 | 1 | 2.05 |