Assuming that there are different kinds of compression techniques (/psymodels?) and each sample has its own optimal technique, it would follow that an optimal compression algorithm would be an adaptive one. Where for each sample a prediction is being made as to which technique to use.
I am confused rate-distortion theory is this what you are thinking of?
Quote from: HotshotGG on 21 November, 2006, 06:08:31 PMI am confused rate-distortion theory is this what you are thinking of? No, I don't think so. Although I'm not well aware with this theory.I'm wondering, for instance, if lossy compression techniques, like lame, use multiple perceptual models when compressing a single track. Or that it uses a single perceptual model (perhaps in the assumption that a single model is optimal for all samples/tracks). I hope I made it more clear this time.Edit: Perhaps I haven't got a good idea what a perceptual model is. I'm assuming there are multiple models, and perhaps that encoders use only one in general.
I'm wondering, for instance, if lossy compression techniques, like lame, use multiple perceptual models when compressing a single track. Or that it uses a single perceptual model (perhaps in the assumption that a single model is optimal for all samples/tracks).
Well, a psy model is a way of deciding mathematically the best way to encode something. So you're suggesting there would be some way of deciding which way of deciding the best way of encoding something would be? How do you propose to do that?
You'd need some sort of psy model to decide between different psy models!
Perhaps certain practical issues has to be considering when speccing a codec, such as finite complexity, delay, cpu demands, etc.
The answer would be no that would be to infinitely complex. If you take into account ATH levels, masking, etc.
What is a sample? It is a measure of signal level, nothing more. Individual samples have no audio meaning; they must be utilized within the gestalt of what went before and what comes next.
However, I am guessing that the benefit, if any, would be very small compared to the effort. And that the knowledge gained through those large-scale listening tests should rather be pumped into improving and replacing codecs?
For practising engineers and other optimization professionals, the theorem justifies the view that as much prior domain knowledge should be utilized as possible, and custom optimization routines constructed for particular domains.