Name
Abstract | ||
|---|---|---|
This work proposes a novel scheme of compressing and decompressing encrypted image based on compressive sensing. An original image is encrypted as a set of coefficients by a secret orthogonal transform. Since the image has sparse representation in conventional transform domain and can be recovered from a small quantity of measurements, the encrypted image data are compressed into a series of measurement data. Using signal recovery method of compressive sensing, a receiver can reconstruct the principal content of original image. This way, the quality of reconstructed image is dependent on the compression rate and the smoothness of original content. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IIHMSP.2011.12 | IIH-MSP |
Keywords | Field | DocType |
measurement data,original content,encrypted image,principal content,reconstructed image,novel scheme,compression rate,compressive sensing,encrypted image data,secret orthogonal,original image,data compression,cryptography,vectors,compressed sensing,image reconstruction,image compression | Iterative reconstruction,Top-hat transform,Computer vision,Data compression ratio,Pattern recognition,Computer science,Sparse approximation,Image processing,Artificial intelligence,Data compression,Digital image processing,Image compression | Conference |
Citations | PageRank | References |
18 | 0.94 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinpeng Zhang | 1 | 2541 | 174.68 |
| Yanli Ren | 2 | 247 | 24.83 |
| Guorui Feng | 3 | 19 | 4.68 |
| Zhenxing Qian | 4 | 525 | 39.26 |