R-D optimized tree-structured compression algorithms with discrete directional wavelet transform
Journal of Computational and Applied Mathematics
Low-complexity iris coding and recognition based on directionlets
IEEE Transactions on Information Forensics and Security
Multiresolution image representation using combined 2-D and 1-D directional filter banks
IEEE Transactions on Image Processing
Discrete directional wavelet image coder based on fast R-D optimized quadtree decomposition
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Low-complexity iris recognition with oriented wavelets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Adaptive directional wavelet transform based on directional prefiltering
IEEE Transactions on Image Processing
Direction-adaptive partitioned block transform for color image coding
IEEE Transactions on Image Processing
An image compression method based on the multi-resolution characteristics of BEMD
Computers & Mathematics with Applications
Joint optimization coding for level and map information in H.264/AVC
Image Communication
Computers and Electrical Engineering
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The standard separable 2-D wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to efficiently capture 1-D discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm.