All a compression does is choose what will compress to less than the input size, and what will compress to greater or the same input size. Pigeon hole principle.. Whatever gains are supposedly had over zlib mean that these other compressions are assigning meaning to other aspects that might not deserve to be pigeon holed to smaller input sizes.
Theres no magic in compression, if I make unintelligable garbage artifacts compress, I lose that domain for compressing intelligible repeats or other reasonable patterns.
A good compression always values patterns OF intelligible deliberate meaning -- theres no holy grail. Zlib is very competent at this.
Modern compression has an entropy code step where you have a probability of seeing a symbol. And yet DEFLATE, the algorithm zlib is based on, uses Huffman coding, which means probabilities can only be integers. Arithmetic coders (like Brotli uses) and ANS-based coders (like BitKnit uses) have support for fractional probabilities. So already, the improved entropy encode step means that you get some gains. They're not perfect in any case, but they're a wide improvement on most data.
zlib, the library, isn't even perfect. You can easily get more out of DEFLATE by simply replacing the compressor with a smarter one, e.g. Zopfli.
Comments
All a compression does is choose what will compress to less than the input size, and what will compress to greater or the same input size. Pigeon hole principle.. Whatever gains are supposedly had over zlib mean that these other compressions are assigning meaning to other aspects that might not deserve to be pigeon holed to smaller input sizes.
Theres no magic in compression, if I make unintelligable garbage artifacts compress, I lose that domain for compressing intelligible repeats or other reasonable patterns.
A good compression always values patterns OF intelligible deliberate meaning -- theres no holy grail. Zlib is very competent at this.
Modern compression has an entropy code step where you have a probability of seeing a symbol. And yet DEFLATE, the algorithm zlib is based on, uses Huffman coding, which means probabilities can only be integers. Arithmetic coders (like Brotli uses) and ANS-based coders (like BitKnit uses) have support for fractional probabilities. So already, the improved entropy encode step means that you get some gains. They're not perfect in any case, but they're a wide improvement on most data.
zlib, the library, isn't even perfect. You can easily get more out of DEFLATE by simply replacing the compressor with a smarter one, e.g. Zopfli.