Abstract
The Asymmetric Numeral System (ANS) is a new entropy compression method that the industry has highly valued in recent years. ANS is valued by the industry precisely because it captures the benefits of both Huffman Coding and Arithmetic Coding. Surprisingly, compared with Huffman and Arithmetic coding, systematic descriptions of ANS are relatively rare. In 2017, JPEG proposed a new image compression standard—JPEG XL, which uses ANS as its entropy compression method. This fact implies that the ANS technique is mature and will play a kernel role in compressing digital images. However, because the realization of ANS involves combination optimization and the process is not unique, only a few members in the compression academia community and the domestic industry have noticed the progress of this powerful entropy compression approach. Therefore, we think a thorough overview of ANS is beneficial, and this idea brings our contributions to the first part of this work. In addition to providing compact representations, ANS has the following prominent feature: just like its Arithmetic Coding counterpart, ANS has Chaos characteristics. The chaotic behavior of ANS is reflected in two aspects. The first one is that the corresponding compressed output will change a lot if there is a tiny change in the original input; moreover, the reverse is also applied. The second is that ANS compressing an image will produce two intertwined outcomes: a positive integer (aka. state) and a bitstream segment. Correct ANS decompression is possible only when both can be precisely obtained. Combining these two characteristics helps process digital images, e.g., art collection images and medical images, to achieve compression and encryption simultaneously. In the second part of this work, we explore the characteristics of ANS in depth and develop its applications specific to joint compression and encryption of digital images.
Highlights
In our review paper, we present the operational details and possible applications of the newly developed lossless compression algorithm—Asymmetric Numeral System (ANS)
The x-axis of the middle picture denotes the image’s RGB value, which ranges from 0 to 255; the corresponding y-axis represents the number of appearances of each RGB value
Notice that the higher the height of the bar is in the (c) chart, the poorer the compression performance
Summary
We present the operational details and possible applications of the newly developed lossless compression algorithm—Asymmetric Numeral System (ANS). Its lossless compression feature makes ANS especially suitable for distortion-less compression-related applications, such as medical and digital art collection images. If we slightly change the compressed representation, the reconstructed version will present a rather significant change after decompression This kind of significant range’s difference between input and output of a function is one of the preferred features and is called the avalanche effect in cryptography [9]. If the input changes a little, the corresponding integer state and the bitstream segment of the output will change significantly. The avalanche feature mentioned above is suitable for providing a compact representation of digital art collection images. A digital art image is represented by a positive integer state and a bitstream sequence. As application examples, we will explore the feasibility of using ANS to protect IPRs of art collection images and check the integrity of medical images
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