Title :
Image data compression using multiple bases representation
Author :
Tilki, John F. ; Beex, A. A Louis
Author_Institution :
Bradley Dept. of Electr. Eng., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA
Abstract :
Digitized images contain huge amounts of information which strain, or exceed, the capacity for their real-time processing, storage, and retrieval. Various compression techniques have been developed to reduce the amount of data necessary for representation. The authors report on a hybrid image data compression procedure based on a multiple bases representation. The multiple bases representation technique described utilizes advantages of transform coding, vector quantization, and predictive coding, while aiming to circumvent the associated disadvantages of each. Preliminary results indicate that this procedure can outperform conventional compression methods, and yield high compression ratios while avoiding prohibitive computational complexity
Keywords :
encoding; image coding; linear predictive coding; vector quantisation; hybrid image data compression; image data compression; multiple bases representation; predictive coding; transform coding; vector quantization; Capacitive sensors; Computational complexity; Data compression; Image coding; Image retrieval; Image storage; Information retrieval; Predictive coding; Transform coding; Vector quantization;
Conference_Titel :
System Theory, 1994., Proceedings of the 26th Southeastern Symposium on
Conference_Location :
Athens, OH
Print_ISBN :
0-8186-5320-5
DOI :
10.1109/SSST.1994.287833