Efficient chain code compression with interpolative coding

A new approach is proposed for lossless raster image compression employing interpolative coding. A new multifunction prediction scheme is presented first. Then, interpolative coding, which has not been applied frequently for image compression, is explained briefly. Its simplification is introduced in regard to the original approach. It is determined that the JPEG LS predictor reduces the information entropy slightly better than the multi-functional approach. Furthermore, the interpolative coding was moderately more efficient than the most frequently used arithmetic coding. Finally, our compression pipeline is compared against JPEG LS, JPEG 2000 in the lossless mode, and PNG using 24 standard grayscale benchmark images. JPEG LS turned out to be the most efficient, followed by JPEG 2000, while our approach using simplified interpolative coding was moderately better than PNG. The implementation of the proposed encoder is extremely simple and can be performed in less than 60 lines of programming code for the coder and 60 lines for the decoder, which is demonstrated in the given pseudocodes.

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“…The interval between L and H is filled simply by incrementing the value

. A similar case was recognized in [ 32 , 33 ]. If

), where D is the maximal value of the domain, then the encoder also does not emit any bits.…”

Section: Methodssupporting

confidence: 67%

“…The encoding in step 3 can be completed in different ways: classical binary codes, truncated binary code [ 5 ], FELICS codes [ 34 ], and

codes (in the case of a small alphabet), as suggested in [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

FLoCIC: A Few Lines of Code for Raster Image Compression

Žalik

Strnad

Kohek

et al. 2023

Entropy

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“…5. They were mostly inherited from the tests made in [7,11,12]. Objects from the first two rows were used for testing (see Table 1), while the others belong to the training set.…”

Section: Training Setmentioning

confidence: 99%

“…Further advances in statistics-based approaches were achieved by introducing extra codes for frequent pairs of primitives [7,8], or by utilization of multiple statistical models in so-called context-based approaches [1], where the statistical model for coding a considered symbol is conditioned on the context of M (typically 1 or 2) previous symbols. On the other hand, non-statistical approaches perform various string transformations [11,12], e.g. Burrows-Wheeler Transform (BWT) and/or Move-To-Front Transform (MTFT) to increase the number of 0's and prepare the data for efficient run-length encoding (RLE) and/or binary arithmetic coding (BAC).…”

Section: Introductionmentioning

confidence: 99%

Statistics-Based Chain Code Compression with Decreased Sensitivity to Shape Artefacts

Podgorelec

Nerat

Žalik

2021

IJCAI

Self Cite

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Chain codes compactly represent raster curves, but there is still a lot of room for improvement by means of data compression. Several statistics-based chain code compression techniques assign shorter extra codes to frequent pairs of consecutive symbols. Here we systematically extend this concept to patterns of up to k > 2 symbols. A curve may be represented by any of the exponentially many overlapped chains of codes, and the dynamic programming approach is proposed to determine the optimal chain. We also propose utilization of multiple averaged hard coded pseudo-statistical models, since the exact statistical models of individual curves are often huge, and they can also significantly differ from each other. A competitive compression efficiency is assured in this manner and, as a pleasant side effect, this efficiency is less affected by the curve's shape, rasterization algorithm, noise, and image resolution, than in other contemporary methods, which surprisingly do not pay any attention to this problem at all. Povzetek: V članku predstavimo novo metodo za statistično stiskanje verižnih kod, ki dodeli posebne kode pogostim nizom do k simbolov. Optimalno izmed eksponentno mnogo rešitev izbere z dinamičnim programiranjem. Uporablja več povprečenih psevdo-statističnih modelov, ki jih ne shranjuje skupaj s krivuljo. V primerjavi z drugimi sodobnimi metodami doseže konkurenčno stopnjo stiskanja, hkrati pa je manj občutljiva na obliko krivulje, posebnosti rasterizacijskega algoritma, šum in ločljivost slike.

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“…14 Besides its main application in inverted indexes, Interpolative coding has previously been successfully applied to the entropy coding of MTF numbers 15 and sorted bilevel images. 16 Recently, Žalik 17 et al used interpolative coding as the final stage of a Burrows-Wheeler-based compression for chain codes. In contrast to the method of Žalik et al, our scheme is intended for generic lossless (text) compression, takes advantage of Teuhola's breadth-first interpolative coding approach, and introduces a new reversible sorting stage between MTF and interpolative coding, substantially improving the clustering and thereby the compression rate.…”

Section: Local-to-global Transformmentioning

confidence: 99%

Burrows‐Wheeler post‐transformation with effective clustering and interpolative coding

Niemi

Teuhola

2020

Softw Pract Exp

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Lossless compression methods based on the Burrows-Wheeler transform (BWT) are regarded as an excellent compromise between speed and compression efficiency: they provide compression rates close to the PPM algorithms, with the speed of dictionary-based methods. Instead of the laborious statistics-gathering process used in PPM, the BWT reversibly sorts the input symbols, using as the sort key as many following characters as necessary to make the sort unique. Characters occurring in similar contexts are sorted close together, resulting in a clustered symbol sequence. Run-length encoding and Move-to-Front (MTF) recoding, combined with a statistical Huffman or arithmetic coder, is then typically used to exploit the clustering. A drawback of the MTF recoding is that knowledge of the character that produced the MTF number is lost. In this paper, we present a new, competitive Burrows-Wheeler posttransform stage that takes advantage of interpolative coding-a fast binary encoding method for integer sequences, being able to exploit clusters without requiring explicit statistics. We introduce a fast and simple way to retain knowledge of the run characters during the MTF recoding and use this to improve the clustering of MTF numbers and run-lengths by applying reversible, stable sorting, with the run characters as sort keys, achieving significant improvement in the compression rate, as shown here by experiments on common text corpora. K E Y W O R D S lossless compression, Burrows-Wheeler transform, move-to-front, interpolative coding This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Efficient chain code compression with interpolative coding

Cited by 22 publications

References 29 publications

FLoCIC: A Few Lines of Code for Raster Image Compression

FLoCIC: A Few Lines of Code for Raster Image Compression

Statistics-Based Chain Code Compression with Decreased Sensitivity to Shape Artefacts

Burrows‐Wheeler post‐transformation with effective clustering and interpolative coding

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