Abstract
A fast image reconstruction method for Image Compression based on Fuzzy relational equations (ICF) and soft computing is proposed. In experiments using 20 images (Standard Image DataBAse), the decrease in image reconstruction time to 1/132.02 and 1/382.29 are obtained when the compression rate is 0.0156 and 0.0625, respectively, and the proposed method outperforms the conventional one in the Peak Signal to Noise Ratio (PSNR). ICF using nonuniform coders over YUV color space is proposed in order to achieve effective compression. Linear quantization of compressed image data is introduced in order to improve the compression rate. Through experiments using 100 typical images (Corel Gallery, Arizona Directory), the PSNR increases at 7.9%-14.1% compared with the conventional method under the condition that compression rates are 0.0234-0.0938.
| Original language | English |
|---|---|
| Pages (from-to) | 72-80 |
| Number of pages | 9 |
| Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
| Volume | 8 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2004 |
| Externally published | Yes |
Keywords
- Fuzzy Relation
- Image Compression and Reconstruction
- Linear Quantization
- Soft Computing