I have a large sequence of random integers sorted from the lowest to the highest. The numbers start from 1 bit and end near 45 bits. In the beginning of the list I have numbers very close to each other: 4, 20, 23, 40, 66. But when the numbers start to get higher the distance between them is a bit higher too (actually the distance between them is aleatory). There are no duplicated numbers.
I’m using bit packing to save some space. Nonetheless, this file can get really big.
I would like to know what kind of compression algorithm can be used in this situation, or any other technique to save as much space as possible.
Thank you.
You can compress optimally if you know the true distribution of the data. If you can provide a probability distribution for each integer you can use arithmetic coding or other entropy coding techniques to compress to theoretical minimal size.
The trick is in predicting accurately.
First, you should probably compress the distances between the numbers because that allows you to make statistical statements. If you were to compress the numbers directly you’d have a hard time modelling them because they occur only once.
Next, you could try to build a very simple model to predict the next distance. Keep a histogram of all previously seen distances and calculate the probabilities from the frequencies.
You probably need to account for missing values (you clearly can’t assign them 0 probability because that is not expressible) but you can use heuristics for that, like encoding the next distance bit-by-bit and predicting each bit individually. You will pay almost nothing for the high-order bits because they are almost always 0 and entropy encoding optimizes them away.
All of this is much simpler if you know the distribution. Example: You you are compressing a list of all prime numbers you know the theoretical distribution of distances because there are formulae for that. So you already have a perfect model.