A bitmap, also referred to as bit-vector or bit-array, is a sequence of 0s and 1s which typically encodes a more complex object.
A common example of this is a set of numbers where each of the elements are indicated as set bits in a bitmap of length equal to the greatest element plus one (as we count from zero), also commonly referred as the universe. As an example, the set {3,5,21,4,23,12}
can be represented as 101000000001000000111000, where - counting right to left - we have the 1-bit at the corresponding positions of the elements in the initial set.
The importance of bitmaps is irrefutable, this is why I recently started investigating which are the most effective techniques used to compress them. Being able to reduce their memory usage means being able to store more data or, possibly, fit it in a lower level of the cache hierarchy which immediately translates to faster access.
The technique I would like to discuss sets the base for more complex ones, which I will try to cover in a future blog post. The most important property of the following compression algorithm is the ability to query the bitmap without fully decompressing it. Considering the set we saw in the previous example, this would be extremely appealing, as we would be able to tell if an element i is present or not just by looking at the bit at position i.
The compression I am going to present falls into the category of data structures called succinct data structures, which allow efficient query operations while using an amount of space that is close to the information-theoretic lower bound.
Now we split the bitmap into fixed-length blocks. In the previous example, the bitmap was 24-bit long, if we split it into blocks of 3-bits each we obtain four distinct blocks.
The idea is to code each block independently from the the others, using a pair of values <C_{i},O_{i}> for the i-th block. The first element of the pair is the cardinality of the block, also referred as population count or just popcount; while the latter is the offset in the table that contains all the distinct permutations (so combinations) of the bits in that block ^{1}.
Let’s say we want to encode the first block 101. Calculating C is trivial as we need to count the number of bits set to 1. This can also be done in hardware by most of the modern CPUs (I will come back to this topic again in the future), but for now, we can rely on the following naive implementation.
Now lets imagine we have a table containing all the \(\binom{3}{2} = 3\) ordered permutations of the previous block. If we iterated over the rows of this table and stop when we reach the entry that matches our block, then we would have computed the offset for that block. In our example, the offset of the block in the following table would be 2.
0 | 011 |
1 | 101 |
2 | 110 |
In this way we can encode our block with the two integers C = 2 and O = 2.
Whenever we would like to decode the block from the given C and O we need to select the appropriate table of combinations using C and then move to the index O of that table to retrieve the original representation.
In this environment if we were only interested in the i-th bit, we would have decoded the entire block and applied a proper mask to filter it out.
So far we realized we need to store a pair for integers for each block, so for \(m/b\) blocks where m is the original bitmap length and b is the fixed block size. We know that the population count of the block cannot be greater then the block size its-self, since there cannot be more than b set bits in a block. Then we can state that the C coefficients can be stored in \(\log_2(b)\) bits. Regarding the offsets we know that they are indexes in a table, but the table size depends on two factors: the blocks size and the number of set bits in it. We know that the former is the same for each block, but the population count can vary.
There are two lucky cases where the cardinality gives us enough information to infer the offset:
In these two cases we can store the offset implicitly and so we would not sacrifice any extra space. For all the other possibilities we can always store the offset in \(\log_2\binom{b}{C}\) bits.
For instance, the original bitmap we used in the previous example used 24 bits of actual data in its uncompressed form. To store the cardinality of each block we would need 2 bits per block, for a total of 16 bits. Then, the blocks containing all-zeros or all-ones is encoded implicitly, while the blocks with C = 1 and C = 2 need 2 additional bits each. This sums up to 20 bits used to represent our uncompressed 24-bit bitmap, with a saving of 2 bits or ~16% of the initial size.
At this point we know how to encode and decode a block, what is actually missing is the way to generate the lookup table of the permutations. The answer is that we don’t do it, but instead ordered binary permutations are generated on-the-fly ^{2}.
For small blocks it would actually be doable and probably also convenient, but if the block gets bigger then it is just not feasible. What is needed is an algorithm to compute offsets for a given block and being able to go back from the offset to the original block representation in a reasonably effective way.
The aim of the computation of the offset is to find the index of a block, given its size and population count, in a table listing all the possible permutations. Moreover, it needs to be deterministic and without the overhead of an actual table. Basically what the following algorithm does is iterating over every bit in the block, if is unset it moves to the next bit (so the block size decreases but the cardinality does not vary), else if it is set then we increase the offset by a quantity equal to \(\binom{n-1}{count}\) (now both block size and cardinality decrease by one) where n is the position of the bit and count the missing set bits to encounter.
Now we need to reverse the encoding process. If \(offset \geq \binom{n-1}{count}\), then the first bit of the block was a 1 and we decrement both n and count and subtract \(\binom{t-1}{count}\) from the offset; otherwise it was a 0 and we decrement only n. Every iteration the block size decreases by one. We can stop when we have processed the whole block or when the count reaches 0 as it means that the remaining bits are all unset.
Since most of the times we are interested in a single bit of the block and blocks can be quite long and so slower to decode, we can perform two forms of optimization. The former is to stop the iteration when we reach the given position, the latter is to perform a binary search instead of the linear scan we already described. The combination of the two solutions is ideal, indeed a linear scan is still faster when the position we are interested in is within the first \(block\_size \cdot \log_2(block\_size)\) elements.
I feel this is a nice and elegant way to compress a bitmap while keeping the ability to decode a block in constant time as it on-the-fly decoding only depends on the the block size which is fixed. I am also sure that further improvements for faster decoding can be possible with the use of SIMD instructions.
Feel free to get in touch if you want to share any feedback or have any ideas about the topic and would like to dig more into it.
Rajeev Raman, Venkatesh Raman, and S. Srinivasa Rao. 2002. Succinct indexable dictionaries with applications to encoding k-ary trees and multisets. In Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms (SODA ‘02). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 233-242. ↩
Gonzalo Navarro and Eliana Providel. 2012. Fast, small, simple rank/select on bitmaps. In Proceedings of the 11th international conference on Experimental Algorithms (SEA’12), Ralf Klasing (Ed.). Springer-Verlag, Berlin, Heidelberg, 295-306. ↩