Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Nonparametric Segmentation of Curves into Various Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Progressive Wavelet Image Coding Based on a Conditional Probability Model
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
CSECS'06 Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing
Contourlet based lossy image coder with edge preserving
SSIP'06 Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing
The wavelet based contourlet transform and its application to feature preserving image coding
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Improved edge preserving lossy image compression using wavelet transform
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
| Hi-index | 0.00 |
Abstract: Many progressive wavelet-based image coders are designed for good performance on natural images. They attempt to achieve the greatest reduction in mean squared error (MSE) with each bit sent, an approach that is most effective when the image is composed chiefly of low-frequency content. Many images, however, include sharp-edged objects, text characters or graphics that are not well handled by standard wavelet-based methods. These features, which may contain information important for recognition, become distorted and obscured when highly compressed by standard wavelet-based methods. In this paper, we present a new progressive image coder that treats an image as being composed of three types of information: edges, texture, and edge-associated detail. The locations of important edges are encoded using line graphic techniques. Texture is encoded using a wavelet-based zerotree approach. Detail near edges--that cannot be efficiently encoded as texture--is encoded separately with a bitplane coding technique. With this approach, features in the image that may be important for recognition are well preserved, even at low bit rates.