'No Free Lunch'-Algorithms in Lossy formats? Options

[Lynch]

While daydreaming today I asked myself whether lossy formats use techniques to recognise which 'compressing' technique should be used on which sample. Somewhat in the vain of the "No Free Lunch" theorem (Link). Implying that there is no optimal technique for all samples.

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.

Could anyone share light on whether similar techniques are already being used, or whether I'm talking complete rubbish? I'm new and a bit rusty in the computer science department, so my deepest apologies.

[knutinh]

Perhaps certain practical issues has to be considering when speccing a codec, such as finite complexity, delay, cpu demands, etc.

Perhaps this means that a state of the art codec performs worse for certain material, for certain bitrates etc, compared to the current state of the art knowledge about human perception.

It has been said that although mp3 is generally more efficient at low bitrates, mp2 may(?) be better at very high bitrates.

Perhaps knowledge of this gathered through extensive listening tests means that an ensemble of multiple codecs can gain a little compared to each on its own.

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?

-k