Image compression and decompression using adaptive interpolation

A simple and fast lossy compression and decompression algorithm for digital images is proposed. The method offers varying compression ratios (depending on dimensions of the image) and the acquired decompressed image is close to the original one. A selectable tradeoff between storage size and image quality is allowed, making it possible to adjust degree of compression. Compared to JPEG, it provides us better compression ratio. The suggested method does not restrict itself to any particular type of image.

[1]  M. Mrak,et al.  Picture quality measures in image compression systems , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[2]  Charles J. Rosenberg,et al.  A lossy image compression algorithm based on nonuniform sampling and interpolation of the image intensity surface , 1990 .

[3]  Layne T. Watson,et al.  A Gaussian derivative based version of JPEG for image compression and decompression , 1995, IEEE Trans. Image Process..

[4]  I. Vilovic,et al.  An Experience in Image Compression Using Neural Networks , 2006, Proceedings ELMAR 2006.

[5]  Anil K. Jain,et al.  Image data compression: A review , 1981, Proceedings of the IEEE.

[6]  Nariman Farvardin,et al.  A perceptually motivated three-component image model-part II: applications to image compression , 1995, IEEE Trans. Image Process..

[7]  J. Morel,et al.  An axiomatic approach to image interpolation. , 1998, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[8]  Casas Pla,et al.  Image Compression based on Perceptual Coding Techniques , 1996 .

[9]  Claude Labit,et al.  Irregular image sub-sampling and reconstruction by adaptive sampling , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.