Name
Abstract | ||
|---|---|---|
AbstractThe traditional image Compressive Sensing (CS) conducts block-wise sampling with the same sampling rate. However, some blocking artifacts often occur due to the varying block sparsity, leading to a low rate-distortion performance. To suppress these blocking artifacts, we propose to adaptively sample each block according to texture features in this paper. With the maximum gradient in 8-connected region of each pixel, we measure the texture variation of each pixel and then compute the texture contrast of each block. According to the distribution of texture contrast, we adaptively set the sampling rate of each block and finally build an image reconstruction model using these block texture contrasts. Experimental results show that our adaptive sampling scheme improves the rate-distortion performance of image CS compared with the existing adaptive schemes and the reconstructed images by our method achieve better visual quality. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/3902543 | Periodicals |
Field | DocType | Volume |
Iterative reconstruction,Computer vision,Texture compression,Image texture,Adaptive sampling,Computer science,Sampling (statistics),Artificial intelligence,Pixel,Texture filtering,Compressed sensing | Journal | 2017 |
Issue | ISSN | Citations |
1 | 1687-7578 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang Sun | 1 | 1 | 2.74 |
| Dongyue Xiao | 2 | 0 | 0.34 |
| Wei He | 3 | 10 | 6.83 |
| Ran Li | 4 | 30 | 6.80 |