I need to compress a random stream data like [25,94,182,3,254, ...]. The number of data are close to 4 million. I currently only get 1.4x ratio by Huffman code. The LZW algorithm I tried is take too much time to compress. I hope to find out an efficiency compression method and still have high compression rate, at least 3x. Is there another algorithm that would be able to compress this random data more better?
What is the best lossless compression algorithm for random data
3.5k views Asked by Vincent 炜森 At
1
There are 1 answers
Related Questions in ALGORITHM
- C++ using std::vector across boundaries
- Linked list without struct
- Connecting Signal QML to C++ (Qt5)
- how to get the reference of struct soap inherited in C++ Proxy/Service class
- Why we can't assign value to pointer
- Conversion of objects in c++
- shared_ptr: "is not a type" error
- C++ template using pointer and non pointer arguments in a QVector
- C++ SFML 2.2 vectors
- Lifetime of temporary objects
Related Questions in COMPRESSION
- C++ using std::vector across boundaries
- Linked list without struct
- Connecting Signal QML to C++ (Qt5)
- how to get the reference of struct soap inherited in C++ Proxy/Service class
- Why we can't assign value to pointer
- Conversion of objects in c++
- shared_ptr: "is not a type" error
- C++ template using pointer and non pointer arguments in a QVector
- C++ SFML 2.2 vectors
- Lifetime of temporary objects
Related Questions in LOSSLESS-COMPRESSION
- C++ using std::vector across boundaries
- Linked list without struct
- Connecting Signal QML to C++ (Qt5)
- how to get the reference of struct soap inherited in C++ Proxy/Service class
- Why we can't assign value to pointer
- Conversion of objects in c++
- shared_ptr: "is not a type" error
- C++ template using pointer and non pointer arguments in a QVector
- C++ SFML 2.2 vectors
- Lifetime of temporary objects
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
Popular Tags
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
It depends on the distribution of the rng. A compression ratio of 1:1.4 suggest that it's not uniform or not good. Huffman and arithmetic coding are practically the only options*, since there is no other correlation between successive entries of good RNG.
*To be precise, the best compression scheme has to be 0-order statistical compression that is able to allocate a variable number of bits for each symbol to reach the Shannon entropy
H(x) = -Sigma_{i=1}^{N} P(x_i) log_2 P(x_i)
The theoretical best is achieved by arithmetical coding, but other encodings can come close by chance. Arithmetic coding can allocate less than one bit per symbol, where as Huffman, or Golomb coding need at least one bit per symbol (or symbol group).