The Huffman Coding

At the end of the perceptual coding process, a second compression process is run. However, this second round is not a perceptual coding, but rather a more traditional compression of all the bits in the file, taken together as a whole. To use a loose analogy, you might think of this second run, called the " Huffman coding," as being similar to zip or other standard compression mechanisms (in other words, the Huffman run is completely lossless, unlike the perceptual coding techniques). Huffman coding is extremely fast, as it utilizes a look-up table for spotting possible bit substitutions. In other words, it doesn't have to "figure anything out" in order to do its job.

The chief benefit of the Huffman compression run is that it compensates for those areas where the perceptual masking is less efficient. For example, a passage of music that contains many sounds happening at once (i.e., a " polyphonous" passage) will benefit greatly from the masking filter. However, a musical phrase consisting only of a single, sustained note will not. However, this passage can be compressed very efficiently with more traditional means, due to its high level of redundancy. On average, an additional 20% of the total file size can be shaved during the Huffman coding.

Raw Power

If you've surmised from all of this that encoding and decoding MP3 must require a lot of CPU cycles, you're right. In fact, unless you're into raytracing or encryption cracking, encoding MP3 is one of the few things an average computer user can do on a regular basis that consumes all of the horsepower you can throw at it. Note, however, that the encoding process is far more intensive than decoding (playing). Since you're likely to be decoding much more frequently than you will be encoding, this is intentional, and is in fact one of the design precepts of the MP3 system (and even more so of next-generation formats such as AAC and VQF).

Creating an MP3 file, as previously described, is a hugely complex task, taking many disparate factors into consideration. The task is one of pure, intensive mathematics. While the computer industry is notorious for hawking more processing power to consumers than they really need, this is one area where you will definitely benefit from the fastest CPU (or CPUs) you can get your hands on, if you plan to do a lot of encoding.

It's impossible to recommend any particular processor speed, for several reasons:

 

• People have very different encoding needs and thresholds of what constitutes acceptable speed.

   

• Encoders, as you'll see in Chapter 5, vary radically from one to the next in terms of their overall efficiency.

   

• Any mention of specific processor speeds would surely be out-of-date by the time you read this.

   

• There's more to the equation than just the speed of the CPU. While MHz may be a good measure of CPU speed when comparing processors from the same family, it's not a good performance measurement between systems. The difference in size of the chip's on-board cache may have a big impact on encoding speeds, as do floating-point optimizations, so one must be careful to note such differences when benchmarking.