Title :
Massive neural video compression with temporal subsampling
Author :
Cramer, C. ; Gelenbe, Erol
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
Abstract :
The large amounts of information involved in the transmission and storage of video data requires massive compression for efficient storage and transmission. However state-of-the-art video compression techniques do not achieve compression levels which are large enough to transmit video data on low band-width links. Thus compression levels need to be improved by a factor of 2 to 10 before they will become useful on the kinds of links which may be encountered in personal communications and cellular telephony. In this paper we present a new video compression technique which makes use of temporal subsampling and reconstruction of frames. When used in conjunction with our adaptive neural video compression (ANVC) technique, this new method leads to compression ratios as high as 500:1 for gray scale sequences with little loss of video quality
Keywords :
data compression; image reconstruction; interpolation; neural nets; splines (mathematics); video coding; adaptive neural video compression; cellular telephony; compression ratios; gray scale sequences; low band-width links; massive neural video compression; personal communications; temporal subsampling; video data; video quality; Bandwidth; Computer networks; Discrete cosine transforms; Entropy; Image coding; Image storage; Telephony; Transform coding; Video compression; Video sequences;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.549202
