Abstract
We present a parametric statistical model for visual images in the wavelet transform domain. We characterize the joint densities of coefficient magnitudes at adjacent spatial locations, adjacent orientations, and adjacent spatial scales. The model accounts for the statistics of a wide variety of visual images. As a demonstration of this, we've used the model to design a progressive image encoder with state-of-the-art rate-distortion performance. We also show promising examples of image restoration and texture synthesis.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 673-678 |
| Number of pages | 6 |
| Journal | Conference Record of the Asilomar Conference on Signals, Systems and Computers |
| Volume | 1 |
| State | Published - 1998 |
| Event | Proceedings of the 1997 31st Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA Duration: Nov 2 1997 → Nov 5 1997 |
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications