Improving the quality of images synthesized by discrete cosine transform regression-based method using principle component analysis
| Author | Kian Hamedani | en |
| Author | valiallah saba | en |
| Issued Date | 2014-06-30 | en |
| Abstract | Materials and Methods: Two new methods, based on neural networks and principle component analysis (PCA) were used to make virtual views of an image. The results were compared with those of the DCT-based method. Two distance metrics, i.e. mean square error (MSE) and structural similarity index measure (SSIM), were used to measure and compare image qualities. About 400 data were used to evaluate the performance of the new proposed methods. | en |
| DOI | https://doi.org/ | en |
| Keyword | neural networks | en |
| Keyword | face recognition | en |
| Keyword | principle component analysis | en |
| Keyword | discrete cosine transform | en |
| Keyword | mean square error | en |
| Keyword | stractural simillarity index measurment | en |
| Publisher | Brieflands | en |
| Title | Improving the quality of images synthesized by discrete cosine transform regression-based method using principle component analysis | en |
| Type | Research Article | en |
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