Hi folks. I'm currently chewing on the following problem: I have a bunch of huge motion sensor signals to lossy compress. Since I need random access to the samples block based compression schemes are out of question. The current approach works like this: I store every 8th sample, quantized from 32 bit down to 8 bits and use this as a rough approximation of the signal. Along with this prediction data I store the errors, agan quantized and with a variable bit-width per 8 sample block. I know that this is a very crude approach but unfortunatley the system where I have to decompress the data on is not as powerful as I'd like it to be. I have to decompress several hundrets of streams at nearly an instant. I have fast RAM and a slow CPU. Fortunately compression time is not *that* much of an issue. In the current approach I see one thing where I can improve a lot: My prediction. Taking every n'th sample is far from optimal. I'm sure I can do much better here. I thought about doing a linear least square fit to get my error down. That will help, but what I really want is a prediction that minimizes the the number of significant bits of the error per block. Does some function-fitting like this exist? Links to (free available) papers or keywords for a good google session are welcome.. Thanks, guys.. Nils
motion sensor signal compression/prediction
Started by ●August 13, 2009