JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS
IEEE Transactions on Image Processing
Directional dyadic wavelet transforms: design and algorithms
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Curved wavelet transform for image coding
IEEE Transactions on Image Processing
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
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We recognize that the key challenge in image compression is to efficiently represent and encode high-frequency image structural components, such as edges, patterns, and textures. Existing image compression schemes attempt to predict image data using its spatial neighborhood. In this work, we develop an efficient image compression scheme based on super-spatial prediction of structural units. This so-called super-spatial prediction breaks this neighborhood constraint, attempting to find an optimal prediction of structural components within the whole image domain. We consider only lossless image compression. Our extensive experimental results demonstrate that the proposed scheme is very competitive and even outperforms the state-of-the-art image compression methods.