An Adaptive Multialphabet Arithmetic Coding Based on Generalized Virtual Sliding Window

Belyaev, Evgeny; Forchhammer, Søren; Li, Kai

doi:10.1109/lsp.2017.2705250

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Nevertheless, the implementation in [14] needs multiplications and divisions; therefore, several look-up table-based adaptive binary arithmetic coding implementations were proposed, which many video and image compression standards have adopted. Moreover, there is more advanced research work related to adaptive range coding (Arithmetic Coding with fast renormalization), for example [15], and multiplication and division free multi-symbol Arithmetic Coding [16].…”

Section: Huffman Coding Arithmetic Coding and The Asymmetric Numeral ...mentioning

confidence: 99%

“…According to Table A2, the generated symbol is c, and the corresponding decoded state value would be 7. However, 7 is not in the legal state range I := [16,31], so we should left-shift 7 by 2 (= R − log 2 (7) = 4 − log 2 (7) ) bit and add K (=2) bits taken from the bitstream variable (denoted as y) to the renormalized result. It is easy to check that the output of the decoder becomes 28 + y now.…”

Section: Appendix Cmentioning

confidence: 99%

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A Review of the Asymmetric Numeral System and Its Applications to Digital Images

Hsieh

2022

Entropy

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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.

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Section: Huffman Coding Arithmetic Coding and The Asymmetric Numeral ...mentioning

confidence: 99%

Section: Appendix Cmentioning

confidence: 99%

A Review of the Asymmetric Numeral System and Its Applications to Digital Images

Hsieh

2022

Entropy

View full text Add to dashboard Cite

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“…However, arithmetic coding has a higher computational complexity because it requires multiplication and division during the coding process. To resolve the complexity issue, approximations are used in binary arithmetic coding algorithms, such as [ 3 , 4 , 5 ], etc. These binary arithmetic coding algorithms are practical algorithms because the coding of a multiple symbol source can always be converted to coding of a sequence of binary symbol sources.…”

Section: Introductionmentioning

confidence: 99%

Modified Hilbert Curve for Rectangles and Cuboids and Its Application in Entropy Coding for Image and Video Compression

Rong

Zhang

Lin

2021

Entropy

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In our previous work, by combining the Hilbert scan with the symbol grouping method, efficient run-length-based entropy coding was developed, and high-efficiency image compression algorithms based on the entropy coding were obtained. However, the 2-D Hilbert curves, which are a critical part of the above-mentioned entropy coding, are defined on squares with the side length being the powers of 2, i.e., 2n, while a subband is normally a rectangle of arbitrary sizes. It is not straightforward to modify the Hilbert curve from squares of side lengths of 2n to an arbitrary rectangle. In this short article, we provide the details of constructing the modified 2-D Hilbert curve of arbitrary rectangle sizes. Furthermore, we extend the method from a 2-D rectangle to a 3-D cuboid. The 3-D modified Hilbert curves are used in a novel 3-D transform video compression algorithm that employs the run-length-based entropy coding. Additionally, the modified 2-D and 3-D Hilbert curves introduced in this short article could be useful for some unknown applications in the future.

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On a hybrid lossless compression technique for three‐dimensional medical images

Subramanian

Palanisamy

Prasath

2021

J Applied Clin Med Phys

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In the last two decades, incredible progress in various medical imaging modalities and sensing techniques have been made, leading to the proliferation of three‐dimensional (3D) imagery. Byproduct of such great progress is the production of huge volume of medical images and this big data place a burden on automatic image processing methods for diagnostic assistance processes. Moreover, large amount of medical imaging data needs to be transmitted with no loss of information for the purpose of telemedicine, remote diagnosis etc. In this work, we consider a hybrid lossless compression technique with object‐based features for three‐dimensional (3D) medical images. Our approach utilizes two phases as follows: first we determine the volume of interest (VOI) for a given 3D medical imagery using selective bounding volume (SBV) method, and second the obtained VOI is encoded using a hybrid lossless algorithm using Lembel‐Ziv‐Welch Coding (LZW) followed by arithmetic coding (L to A). Experimental results show that our proposed 3D medical image compression method is comparable with other existing standard lossless encoding methods such as Huffman Coding, Run Length Coding, LZW, and Arithmetic Coding and obtains superior results overall.

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An Adaptive Multialphabet Arithmetic Coding Based on Generalized Virtual Sliding Window

Cited by 7 publications

References 11 publications

A Review of the Asymmetric Numeral System and Its Applications to Digital Images

A Review of the Asymmetric Numeral System and Its Applications to Digital Images

Modified Hilbert Curve for Rectangles and Cuboids and Its Application in Entropy Coding for Image and Video Compression

On a hybrid lossless compression technique for three‐dimensional medical images

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