Video compression with wavelets and random neural network approximations

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.