QUOTE(ye110man @ May 2 2003 - 09:32 PM)
can someone explain why?
in essence aren't all mp3's a subset of cbr 320kbps stereo? different codecs remove different information from the wav so it would obviously result in worse quality but i would think that 320kbps removes information that would be removed by preset standard anyway.
In a perfect information-theoretic view of a lossy codec that'd be correct, if you were using the same psychoacoustic model for all versions (the lower-bitrates would just remove additional information). However, in practice there's additional sources of error. One place is quantization error, which is greatly magnified when re-encoding. As a simple example, consider a 16-bit value that you're going to store with 12 bits. That might work okay; you just have a bit of rounding error. But now if you convert it back to 16 bits and re-quantize to 9 bits, that's a lot worse than a 16->9 quantization would've been.
An additional problem is that codecs cannot tell what's been introduced and what was original. For example, if an encoding produces artifacts (even really minor ones), the re-encoding will waste bits trying to reproduce those exactly, since it sees them as signal, which may end up magnifying them or making other artifacts worse. This is why you can re-encode a file lowpassed at 10 kHz in the first encoding pass and see the encoder trying to store signals over 10 kHz on the second pass.
There's numerous other problems, but these are the biggest sources of quality loss.
Ogg Vorbis is working on something more akin to the information-theoretic view of lossy compression with its bitrate peeling. The idea (very roughly) is that in the first (and only) encoding pass you identify information by various levels of importance, rather than just by "keep" and "throw away". Then you can "peel" the bitrate by progressively throwing out the least-important information. There's no re-quantization, and the encoding is all done on the original, so there's no problems with introduced noise as signal, so both the main problems I noted above are avoided. This is however very hard to do well in practice due to highly non-linear effects; it's very difficult to come up with clean subsets of data like that.