A formula based approach to Arithmetic Coding

In summary, the conversation discusses the concept of entropy encoding and its relationship with other methods such as Huffman coding. The speaker has created an article on the topic and is seeking feedback. The other speaker brings up the importance of understanding the basis of compression algorithms, and mentions that Arithmetic Coding is not usually used for lossy compression. They suggest that understanding the basis can help understand the nature of information and how it is compressed, regardless of the algorithm used.
  • #1
arun-siara
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0
I have been doing research on entropy encoding for some time.. I found some interesting relationships between Arithmetic coding and other methods such as Huffman Coding. I made an article to explain them and am presenting here for review:

http://siara.cc/arithmetic_coding_new_approach/

I have also attached a PDF version for convenience.

Please let me know your ideas.
 

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  • #2
Hey arun-siara.

Just wanted to point out something that could be expanded on or mentioned when it comes to compression and that is the idea of a basis.

Most compression algorithms (particulary the lossy ones - but lossless ones do in one form or another) work by utilizing a basis that has represents the same information density but in a better way.

For example - images and movies like those based on JPEG or MPEG use bases based on the cosine transforms, wavelet transforms, Fourier transforms and other transforms. Each transform has its own basis and what tends to happen is that you retain so many coefficients for given basis vectors that contribute to most of the information density that is being described.

Even though chopping things off is how a lot of lossy algorithms do things, lossless methods also use their own basis. The difference between the two is that they retain all coefficients for all basis vectors that span the space and keep its dimension - so they aren't projections onto sub-spaces but a reconstruction of the information in some space.

If you can think about how the basis are represented and the context of that basis then it will help you relate the different techniques and also make sense of why they work and do the things they do in the way they do.

Just a couple of thoughts.
 
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  • #3
Hi chiro,

Thanks for taking time to go through the article. I agree with you, the basis needs to be mentioned. I thought I would leave it to the articles I have referred which make it abundantly clear. Even then probably the roots and connections would not be clear just by reading the article.

I will need to lookup lossy algorithms again as I don't recall Arithmetic Coding being used for lossy compression.

Thanks again for the constructive feedback. I believe it will make the article look richer.

Regards
Arun

chiro said:
Hey arun-siara.

Just wanted to...
 
  • #4
AC usually isn't a lossy algorithm but the idea of a basis is what makes compression work.

If you understand the basis then you understand the nature of information and how it is actually compressed.

It helps understand that no matter what algorithm you use.
 
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Related to A formula based approach to Arithmetic Coding

1. What is a formula based approach to Arithmetic Coding?

A formula based approach to Arithmetic Coding is a method for compressing data by assigning a unique code to each symbol based on its probability of occurrence. The codes are based on a mathematical formula, rather than a lookup table or other data structure.

2. How does a formula based approach differ from other methods of Arithmetic Coding?

The main difference between a formula based approach and other methods of Arithmetic Coding is the use of a mathematical formula to determine the codes for each symbol. This makes the process more efficient and allows for faster compression and decompression of data.

3. What are the advantages of using a formula based approach to Arithmetic Coding?

One of the main advantages of using a formula based approach is its efficiency. The use of a mathematical formula allows for faster compression and decompression of data. Additionally, this method does not require a lookup table or other data structure, making it more memory efficient.

4. Are there any limitations to using a formula based approach to Arithmetic Coding?

One limitation of this method is that it requires knowledge of the probability distribution of the symbols in the data. If this is not known, the compression may not be as efficient. Additionally, this method may not be suitable for data that is constantly changing or has a large number of symbols.

5. How is a formula based approach to Arithmetic Coding used in real-world applications?

A formula based approach to Arithmetic Coding is commonly used in video and image compression algorithms, such as H.264 and JPEG. It is also used in data compression for telecommunications and data storage, as well as in data compression for DNA sequencing.

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