Video Compression With Wavelets And Random Neural Network Approximations
Neural Networks; Video Compression; Wavelets
Modern video encoding techniques generate variable bit rates, because they take advantage of different rates of motion in scenes, in addition to using lossy compression within individual frames. We have introduced a novel method for video compression based on temporal subsampling of video frames, and for video frame reconstruction using neural network based function approximations. In this paper we describe another method using wavelets for still image compression of frames, and function approximations for the reconstruction of subsampled frames. We evaluated the performance of the method in terms of observed traffic characteristics for the resulting compressed and subsampled frames, and in terms of quality versus compression ratio curves with real video image sequences. Comparisons are presented with other standard methods.
Proceedings of SPIE - The International Society for Optical Engineering
Number of Pages
Article; Proceedings Paper
Source API URL
Hai, F.; Hussain, K.; and Gelenbe, E., "Video Compression With Wavelets And Random Neural Network Approximations" (2001). Scopus Export 2000s. 567.