Lossless Compression of Color Map Images by Context Tree Modeling

Akimov, Alexander; Kolesnikov, Alexander; Fränti, Pasi

doi:10.1109/dcc.2006.42

Cited by 8 publications

(13 citation statements)

References 25 publications

(20 reference statements)

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“…The recent landscape of the field has evolved by the introduction of newer representations utilizing more refined chain codes, which were combined with the advances in context coding to produce much more efficient coding methods [1]. A recent comparison of the most efficient chain code representations of the contours can be found, e.g., in [2], where the 3OT chain-codes were found to be superior or equal to any other type of chain-codes.…”

Section: Introductionmentioning

confidence: 99%

Quaternary Crack-Edge Representation for Lossless Contour Compression

Tăbuş

Schiopu

2013

2013 19th International Conference on Control Systems and Computer Science

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We present a method for lossless compression of contours in images using a specific crack-edge representation, where each horizontal binary crack-edge is bundled together with a vertical binary crack-edge in a new symbol, having four possible values. One can represent all contours in an image using an auxiliary quaternary image, which has bundled at each pixel the value of the north and west crack-edges. We use context coding for the quaternary image with the coding tree optimally pruned to obtain the most relevant bi-dimensional contexts. The coding using bi-dimensional contexts of the crack-edge information contrasts with the coding using chain codes, where the contours are represented by one-dimensional sequences of (differential) angles, telling how one can travel from starting points towards ending points in the bi-dimensional image grid. The code length for encoding the starting and ending points (which are referred to as anchors) is large when the contours are very dense, because the representation with chain codes needs specifying many anchor points, while the newly proposed bi-dimensional context coding does not need such information. In the case of images with very dense contours the quaternary representation can be efficiently encoded by context coding. We present experiments with depth images, where using various quantization steps one can obtain a multitude of images of geodesic contours, having a decreasing density of contour links as the quantization step increases. We illustrate the two coding methods for encoding the contours in fractal images, showing a better performance of the quaternary representation as compared to chain code representation.

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Section: Introductionmentioning

confidence: 99%

Quaternary Crack-Edge Representation for Lossless Contour Compression

Tăbuş

Schiopu

2013

2013 19th International Conference on Control Systems and Computer Science

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“…Context tree coding is a mature field, containing a rich literature proposing various adaptive and semi-adaptive algorithms and their applications, see e.g., [16], [31], [33], [34]. In the HiCo variant we use a semi-adaptive version requiring two-passes through the image.…”

Section: ) Optimal Pruning the Context Treementioning

confidence: 99%

Context Coding of Depth Map Images Under the Piecewise-Constant Image Model Representation

Tăbuş

Schiopu

Astola

2013

IEEE Trans. on Image Process.

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Abstract-This paper introduces an efficient method for lossless compression of depth map images, using the representation of a depth image in terms of three entities: 1) the crack-edges; 2) the constant depth regions enclosed by them; and 3) the depth value over each region. The starting representation is identical with that used in a very efficient coder for palette images, the piecewise-constant image model coding, but the techniques used for coding the elements of the representation are more advanced and especially suitable for the type of redundancy present in depth images. Initially, the vertical and horizontal crack-edges separating the constant depth regions are transmitted by 2D context coding using optimally pruned context trees. Both the encoder and decoder can reconstruct the regions of constant depth from the transmitted crack-edge image. The depth value in a given region is encoded using the depth values of the neighboring regions already encoded, exploiting the natural smoothness of the depth variation, and the mutual exclusiveness of the values in neighboring regions. The encoding method is suitable for lossless compression of depth images, obtaining compression of about 10-65 times, and additionally can be used as the entropy coding stage for lossy depth compression.

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“…Additional compression is gained by using causal context in the encoding procedure. The context-tree based method of [27] is expanded in [28] to "operate on color values instead of binary layers". The expanded methodology of [28] includes creating a context tree of predetermined depth and removing nodes that do not improve compression.…”

mentioning

confidence: 99%

“…The context-tree based method of [27] is expanded in [28] to "operate on color values instead of binary layers". The expanded methodology of [28] includes creating a context tree of predetermined depth and removing nodes that do not improve compression. The gain in compression performance from context-based methods is countered by the need to store the context tree in the compressed image.…”

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confidence: 99%

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Exclusive-or preprocessing and dictionary coding of continuous-tone images.

Cooper¹

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The field of lossless image compression studies the various ways to represent image data in the most compact and efficient manner possible that also allows the image to be reproduced without any loss. One of the most efficient strategies used in lossless compression is to introduce entropy reduction through decorrelation. This study focuses on using the exclusive-or logic operator in a decorrelation filter as the preprocessing phase of lossless image compression of continuous-tone images. The exclusive-or logic operator is simply and reversibly applied to continuous-tone images for the purpose of extracting differences between neighboring pixels.Implementation of the exclusive-or operator also does not introduce data expansion. Traditional as well as innovative prediction methods are included for the creation of inputs for the exclusiveor logic based decorrelation filter. The results of the filter are then encoded by a variation of the Lempel-Ziv-Welch dictionary coder. Dictionary coding is selected for the coding phase of the algorithm because it does not require the storage of code tables or probabilities and because it is lower in complexity than other popular options such as Huffman or Arithmetic coding. The first modification of the Lempel-Ziv-Welch dictionary coder is that image data can be read in a sequence that is linear, 2-dimensional, or an adaptive combination of both. The second modification of the dictionary coder is that the coder can instead include multiple, dynamically chosen dictionaries. Experiments indicate that the exclusive-or operator based decorrelation filter when combined with a modified Lempel-Ziv-Welch dictionary coder provides compression comparable to algorithms that represent the current standard in lossless compression. The Graphics implementation of the Deflate algorithm by 7%, but above the Zip implementation of the Deflate algorithm by 24%. The proposed algorithm uses the exclusive-or operator in the modeling phase and uses modified Lempel-Ziv-Welch dictionary coding in the coding phase to form a low complexity, reversible, and dynamic method of lossless image compression.

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Lossless Compression of Color Map Images by Context Tree Modeling

Cited by 8 publications

References 25 publications

Quaternary Crack-Edge Representation for Lossless Contour Compression

Quaternary Crack-Edge Representation for Lossless Contour Compression

Context Coding of Depth Map Images Under the Piecewise-Constant Image Model Representation

Exclusive-or preprocessing and dictionary coding of continuous-tone images.

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