Modeling techniques for VBR video: feasibility and limitations
Performance Evaluation
| Hi-index | 0.00 |
We summarize a novel neural network technique for video compression, using a point-process type neural network model we have developed [l, 2, 3, 4] which is closer to biophysical reality and is mathematically much more tractable than standard models. Our algorithm uses an adaptive approach based upon the users' desired video quality Q, and achieves compression ratios of up to 500 : 1 for moving gray-scale images, based on a combination of motion detection, compression and temporal subsampling of frames. This leads to a compression ratio of over 1000 : 1 for full-color video sequences with the addition of the standard 4:l:l spatial subsampling ratios in the chrominance images. The Signal-to-Noise-Ratio obtained varies with the compression level and ranges from 29dB to over 34dB. Our method is computationally fast so that compression and decompression could possibly be performed in real-time software.