Context tree compression of multi-component map images

Kopylov, P.; Fränti, Pasi

doi:10.1109/dcc.2002.999959

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Context-tree-based methods for compressing map images have also been proposed [17]. PMIC-a pattern matching-based scheme for lossy image compression was proposed in [18].…”

Section: Results-context Countsmentioning

confidence: 99%

“…Thus, we use a selectable range to reduce the number of symbols needed to code the prediction error if if and if and (16) where represents the estimated range for the prediction error . If an error value falls outside the range defined by (16), then an escape symbol (Esc) is sent to the compressed stream and the error value is compressed based on a new range given by (17), shown at the bottom of the page. To maximize compression, can be estimated based on the residue image, and then stored as part of the compressed bit stream.…”

Section: B Prediction and Range Selectionmentioning

confidence: 99%

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Prediction by Partial Approximate Matching for Lossless Image Compression

Zhang

Adjeroh

2008

IEEE Trans. on Image Process.

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Context-based modeling is an important step in high-performance lossless data compression. To effectively define and utilize contexts for natural images is, however, a difficult problem. This is primarily due to the huge number of contexts available in natural images, which typically results in higher modeling costs, leading to reduced compression efficiency. Motivated by the prediction by partial matching context model that has been very successful in text compression, we present prediction by partial approximate matching (PPAM), a method for compression and context modeling for images. Unlike the PPM modeling method that uses exact contexts, PPAM introduces the notion of approximate contexts. Thus, PPAM models the probability of the encoding symbol based on its previous contexts, whereby context occurrences are considered in an approximate manner. The proposed method has competitive compression performance when compared with other popular lossless image compression algorithms. It shows a particularly superior performance when compressing images that have common features, such as biomedical images.

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“…Context-tree-based methods for compressing map images have also been proposed [17]. PMIC-a pattern matching-based scheme for lossy image compression was proposed in [18].…”

Section: Results-context Countsmentioning

confidence: 99%

Section: B Prediction and Range Selectionmentioning

confidence: 99%

Prediction by Partial Approximate Matching for Lossless Image Compression

Zhang

Adjeroh

2008

IEEE Trans. on Image Process.

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show abstract

“…11 An attempt to spread the optimized context tree modeling to a multilayer case called multilayer context tree ͑MCT͒ modeling has been made in the case of multilayer geographical map images. 12 Optimal ordering of the layers was shown to give additional improvement. 13 In general, the efficiency of the particular compression method depends on the utilization of color and spatial dependencies ͑see Fig.…”

Section: Introductionmentioning

confidence: 97%

Lossless image compression via bit-plane separation and multilayer context tree modeling

Podlasov

Fränti

2006

J. Electron. Imaging

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Color separation and highly optimized context tree modeling for binary layers have provided the best compression results for color map images that consist of highly complex spatial structures but only a relatively few number of colors. We explore whether this kind of approach works on photographic and palette images as well. The main difficulty is that these images can have a much higher number of colors, and it is therefore much more difficult to exploit spatial dependencies via binary layers. The original contributions of this work include: 1. the application of context-tree-based compression (previously designed for map images) to natural and color palette images; 2. the consideration of four different methods for bit-plane separation; and 3. Extension of the two-layer context to a multilayer context for better utilization of the crosslayer correlations. The proposed combination is extensively compared to state of the art lossless image compression methods.

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“…No special attention was paid to the PPM context model, which was used as a simple black box. Context-tree based methods for compressing map images have also been proposed [19]. PMIC -a pattern matching based scheme for lossy image compression was proposed in [20].…”

Section: Lossless Image Compressionmentioning

confidence: 99%

Lossless compression and neuron structure extraction for fluorescence microscopy confocal neuron images

Zhang¹

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Context tree compression of multi-component map images

Cited by 6 publications

References 30 publications

Prediction by Partial Approximate Matching for Lossless Image Compression

Prediction by Partial Approximate Matching for Lossless Image Compression

Lossless image compression via bit-plane separation and multilayer context tree modeling

Lossless compression and neuron structure extraction for fluorescence microscopy confocal neuron images

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